Computing Surveys (CSUR)

Latest Articles

Software Platforms for Smart Cities: Concepts, Requirements, Challenges, and a Unified Reference Architecture

Information and communication technologies (ICT) can be instrumental in progressing towards smarter city environments, which improve city services, sustainability, and citizens’ quality of life. Smart City software platforms can support the development and integration of Smart City applications. However, the ICT community must overcome... (more)

A Survey of Fault-Tolerance Algorithms for Reconfigurable Nano-Crossbar Arrays

Nano-crossbar arrays have emerged as a promising and viable technology to improve computing performance of electronic circuits beyond the limits of... (more)

A Survey of Research into Mixed Criticality Systems

This survey covers research into mixed criticality systems that has been published since Vestal’s seminal paper in 2007, up until the end of... (more)

Cooperative Multi-Agent Planning: A Survey

Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms, and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent... (more)

Surveying Stylometry Techniques and Applications

The analysis of authorial style, termed stylometry, assumes that style is quantifiably measurable for evaluation of distinctive qualities. Stylometry research has yielded several methods and tools over the past 200 years to handle a variety of challenging cases. This survey reviews several articles within five prominent subtasks: authorship... (more)

An Offensive and Defensive Exposition of Wearable Computing

Wearable computing is rapidly getting deployed in many—commercial, medical, and personal—domains of day-to-day life. Wearable devices... (more)

A Tutorial on Canonical Correlation Methods

Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has, for instance, been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the... (more)

A Survey of Presence and Related Concepts

The presence construct, most commonly defined as the sense of “being there,” has driven research and development of virtual environments (VEs) for decades. Despite that, there is not widespread agreement on how to define or operationalize this construct. The literature contains many different definitions of presence and many... (more)

Understanding Application-Level Caching in Web Applications: A Comprehensive Introduction and Survey of State-of-the-Art Approaches

A new form of caching, namely application-level caching, has been recently employed in web applications to improve their performance and increase... (more)


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About CSUR

ACM Computing Surveys (CSUR) publishes comprehensive, readable tutorials and survey papers that give guided tours through the literature and explain topics to those who seek to learn the basics of areas outside their specialties. These carefully planned and presented introductions are also an excellent way for professionals to develop perspectives on, and identify trends in complex technologies. Recent issues have covered image understanding, software reusability, and object and relational database topics. 

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Forthcoming Articles
Graph Processing on GPUs: A Survey

In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modelled as graph processing. Graph processing, especially the processing of the large scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This paper surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping and specific GPU programming. In this paper, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in details, and explore the research opportunities in future.

Exiting the Risk Assessment maze: A meta-survey

Organizations are exposed to threats that increase the risk factor of their ICT systems and the assurance of their protection is crucial, as their reliance on information technology is a continuing challenge for both security experts and chief executives. To tackle down the threats decision makers should be provided with information needed to understand and mitigate them. Risk assessment forms a means of providing such information and facilitates the development of a security strategy. This paper aims at addressing the problem of selection an appropriate risk assessment method to assess and manage information security risks, by proposing a set of 17 criteria, grouped in 4 categories, for comparing such methods and provide a comparison of the 10 most popular methods based upon them. Finally, the comparison presented in the paper could be utilized by organizations to determine which method is more suitable for their needs.

A Review and Assessment Framework for Mobile Based Emergency Intervention Apps

Smartphone applications to support healthcare are proliferating. A growing and important subset of these apps supports emergency medical intervention to address a wide range of illness-related emergencies in order to speed the arrival of relevant treatment. The emergency response characteristics and strategies employed by these apps are the focus in this study resulting in an mHealth Emergency Strategy Index (MESI). While a growing body of knowledge focuses on usability, safety and privacy aspects that characterize such apps, studies that map the various emergency intervention strategies and suggest criteria to evaluate their role as emergency agents are limited. We survey an extensive range of mHealth apps designed for emergency response along with the related assessment literature and present an index for mobile-based medical emergency intervention apps that can address assessment needs of future mHealth apps.

A Survey of Modelling Trends in Temporal GIS

The main achievements of spatio-temporal modelling in the field of Geographic Information Science over the past three decades are surveyed. This article offers an overview of: (i) the origins and history of Temporal Geographic Information Systems (T-GIS); (ii) relevant spatio-temporal data models proposed; (iii) the evolution of spatio-temporal modelling trends; and (iv) an analysis of the future trends and developments in T-GIS. It also presents some current theories and concepts that have emerged from the research performed, as well as a summary of the current progress and the upcoming challenges and potential research directions for T-GIS. One relevant result of this survey is the proposed taxonomy of spatio-temporal modelling trends, which classifies 186 modelling proposals surveyed from more than 1400 articles.

Analysis of Online Social Network Connections for Identification of Influential Users: Survey and Open Research Issues

Online social networks (OSNs) are structures that help users interact, exchange, and propagate new ideas. The identification of the most influential users in OSNs is a significant process for accelerating information propagation including those in marketing applications, or for hindering the dissemination of unwanted contents such as viruses, negative online behaviors, and rumors. The present paper presents a detailed survey of influential users identification algorithms and their performance evaluation approaches in OSNs. The survey covers recent techniques, applications, and open research issues on the influential users identification in OSNs.

Practical Secure Computation Outsourcing: A Survey

The rapid development of cloud computing promotes a wide deployment of data and computation outsourcing by resource-limited entities to cloud providers. Based on a pay-per-use fashion, clients without enough computational power can easily outsource large-scale computational tasks to the cloud. Nonetheless, the issue of security and privacy is a major concern when customers' confidential or sensitive data is processed and the output is generated in not fully trusted cloud environments. Recently, a number of publications have investigated and designed secure outsourcing schemes for different computational tasks. The aim of this survey is to systemize and present the cutting-edge technologies in this area. It starts by presenting security threats and requirements, followed by other factors that should be considered by secure computation outsourcing constructions. In an organized way, we then dwell on the existing secure computation outsourcing solutions to different computational tasks such as matrix computations, mathematical optimization, etc., treating the confidentiality of data as well as the integrity of result. Finally, we offer a discussion of the literature and provide a list of open challenges in the area.

Mining Electronic Health Records (EHRs): A Survey

The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.

A Survey on Fully Homomorphic Encryption: an Engineering Perspective

It is unlikely that an hacker is able to compromise sensitive data that is stored in an encrypted form. However, when data is to be processed, it has to be decrypted, becoming vulnerable to attacks. Homomorphic encryption fixes this vulnerability by allowing one to compute directly on encrypted data. In this survey, both previous and current Somewhat Homomorphic Encryption (SHE) schemes are reviewed, and the more powerful and recent Fully Homomorphic Encryption (FHE) schemes are comprehensively studied. The concepts that support these schemes are presented, and their performance and security are analyzed from an engineering standpoint.

The Experience Sampling Method on Mobile Devices

The Experience Sampling Method (ESM) is used by scientists from various disciplines to gather insights into the intrapsychic elements of human life. Researchers have used the ESM in a wide variety of studies, with the method seeing increased popularity. Mobile technologies have enabled new possibilities for the use of the ESM, while simultaneously leading to new conceptual, methodological, and technological challenges. In this survey, we provide an overview of the history of the ESM, usage of this methodology in the computer science discipline, as well as its evolution over time. Next, we identify and discuss important considerations for ESM studies on mobile devices, and analyse the particular methodological parameters scientists should consider in their study design. We reflect on the existing tools that support the ESM methodology and discuss the future development of such tools. Finally, we discuss the effect of future technological developments on the use of the ESM and identify areas requiring further investigation.

Augmentation Techniques for Mobile Cloud Computing: A Taxonomy, Survey, and Future Directions

Despite the rapid growth of hardware capacity and popularity in mobile devices, limited resources in battery and processing capacity still lack the ability to meet the increasing mobile users' demands. Both conventional techniques and emerging approaches are brought together to fill this gap between the user demand and mobile device's limited capacity. The cloud computing is an uprising topic in both business and academia in recent years to eliminate the gap. Augmentation techniques such as computation outsourcing and service oriented architectures are proposed by the proposed works, and new challenges regarding the augmentation techniques, energy efficiency, etc, needs to be studied. In this paper, we aim to provide a comprehensive taxonomy and survey of the existing techniques and frameworks for mobile cloud augmentation in terms of both computation and storage. Different from the existing taxonomies in this field, we focus on the techniques aspect, following the idea of realizing a complete mobile cloud computing system. The objective of this survey is to provide a guide on what available augmentation techniques can be adopted in mobile cloud computing systems as well as supporting mechanisms such as decision making and fault tolerance policies for realizing reliable mobile cloud services.

Systematic Literature Review on Usability of Firewall Configuration

Firewalls are network security components that handle incoming and outgoing network traffic based on a set of rules. The process of correctly configuring a firewall is complicated and prone to error, and it worsens as the network complexity grows. A poorly configured firewall may result in major security threats; in case of a network firewall, an organizations security could be endangered, and in the case of a personal firewall, an individual computers security is threatened. A major reason of poorly configured firewalls, as pointed out in the literature, is usability issues. Our aim is to identify existing solutions that help professional and non- professional users to create and manage firewall configuration files, and to analyze the proposals in respect of usability. A systematic literature review with a focus on usability of firewall configuration is presented in the paper. Its main goal is to explore what has already been done in this field. In the primary selection procedure, 1,202 papers were retrieved and then screened. The secondary selection led us to 35 papers carefully chosen for further investigation, of which, 14 papers were selected and summarized....

Spatio-Temporal Data Mining: A Survey of Problems and Methods

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differs from relational data for which computational approaches are developed in the data mining community for multiple decades, in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data mining community. In this article we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data mining problems in each of these categories.

Data Storage Management in Cloud Environments: Taxonomy, Survey, and Future Directions

Storage as a Service (StaaS) forms a critical component of cloud computing by offering the vision of a virtually infinite pool of storage resources. It supports a variety of cloud-based data store classes in terms of availability, scalability, ACID (Atomicity, Consistency, Isolation, Durability) properties, data models, and price options. Despite many open challenges within a cloud-based data store, application providers deploy Geo-replicated data stores in order to obtain higher availability, lower response time, and more cost efficiency. The deployment of Geo-replicated data stores is in its infancy and poses vital challenges for researchers. In this paper, we first discuss the key advantages and challenges of data-intensive applications deployed within and across cloud-based data stores. Then, we provide a comprehensive taxonomy that covers key aspects of cloud-based data store: data model, data dispersion, data consistency, data transaction service, and data cost optimization. Finally, we map various cloud-based data store projects to our proposed taxonomy not only to validate the taxonomy but also to identify areas for future research.

A Survey on Optical Network-on-Chip Architectures

Optical on-chip data transmission enabled by silicon photonics is widely considered a key technology to overcome the bandwidth and energy limitations of electrical interconnects. The possibility of utilizing optical links in the on-chip communication fabric has paved the way to a fascinating new research field - Optical Networks-on-Chip (ONoCs) - which has been gaining large interest in the community. Nanophotonic devices and materials, however, are still evolving, and dealing with optical data transmission on chip makes designers and researchers face a whole new set of obstacles and challenges. Designing efficient ONoCs is a challenging task and requires a detailed knowledge from on-chip traffic demands and patterns down to the physical layout and implications of integrating both electronic and photonic devices. In this paper, we provide an exhaustive review of recent ONoC proposals, discuss their strengths and weaknesses, and outline outstanding research questions. Moreover, we discuss recent research efforts in key enabling technologies, such as on-chip and adaptive laser sources, automatic synthesis tools, and ring heating techniques, which are essential to enable a widespread commercial adoption of ONoCs in the future.

A study on Garbage Collection Algorithms for Big Data Environments

The need to handle (process and store) massive amounts of data (Big Data), is a reality. In areas such as scientific experiments, social networks, credit card fraud detection, and financial analysis, massive amounts of information is generated and processed daily to extract valuable, summarized information. Due to its fast development cycle (i.e., less expensive to develop), mainly because of automatic memory management, and rich community resource, managed object-oriented programming languages (such as Java) are the first choice to develop Big Data platforms (e.g., Cassandra, Spark) on which such Big Data applications are executed. However, automatic memory management comes at a cost. This cost is introduced by the Garbage Collector which is responsible for collecting objects that are no longer being used. In this work, we study current Big Data platforms and their memory profiles to understand why classic algorithms (which are still the most common) are not appropriate and also analyze recently proposed and relevant memory management algorithms, targeted to Big Data environments. We characterize the scalability of recent memory management algorithms in terms of throughput (improves the throughput of the application) and pause time (reduces the latency of the application) when comparing to classic algorithms. We conclude our study by presenting a taxonomy of the described works.

Underwater Wireless Sensor Networks: A New Challenge for Topology Control-based Systems

Underwater wireless sensor networks (UWSNs) --- formed by underwater sensor nodes with sensing, processing, storage and underwater wireless communication capabilities --- will pave the way for a new era of underwater monitoring and actuation applications. UWSN has become a fast growing field. The envisioned landscape of applications that will be enabled by UWSNs has tremendous potential to change the current reality, where no more than 5\% of the volume of the oceans were explored. However, to enable large deployments of UWSNs, networking solutions toward efficient underwater data collection need to be investigated and proposed. The suitable, autonomous and on-the-fly organization of UWSN topology, through topology control algorithms, might mitigate undesired effects of the underwater wireless communication and, consequently, improve networking services and protocols. In this paper, therefore, we highlight the potentials of topology control for underwater sensor networks. We proposed to classify topology control algorithms, based on their principal methodology used to change the network topology, into three major groups: power control, wireless interface mode management and mobility assisted-based techniques. On the basis of the proposed classification, we survey the current state-of-the-art and present an in-depth discussion of topology control solutions designed for UWSNs.

Survey and Analysis of Kernel and Userspace Tracers on Linux: Design, Implementation, and Overhead

As applications and operating systems are becoming more complex, the last decade has seen the rise of many tracing tools all across the software stack. This paper presents a hands-on comparison of modern tracers on Linux systems, both in user space and kernel space. The authors implement microbenchmarks that not only quantify the overhead of different tracers, but also sample fine-grained metrics that unveil insights into the tracers' internals and show the cause of each tracer's overhead. Internal design choices and implementation particularities are discussed, which helps to understand the challenges of developing tracers. Furthermore, this analysis aims to help users choose and configure their tracers based on their specific requirements in order to reduce their overhead and get the most of out of them.

Feature Selection: A Data Perspective

Feature selection has been proven to be effective and efficient in preparing high-dimensional data for data mining and machine learning problems. The objectives include: building simpler and more comprehensible models, improving data mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities of feature selection algorithms. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. In particular, we revisit feature selection research from a data perspective, and review representative feature selection algorithms for generic data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for generic data, we generally categorize them into four groups: similarity based, information theoretical based, sparse learning based and statistical based methods. Finally, to facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms ( Also, we use it as an example to show how to evaluate feature selection algorithms. At last, we also have a discussion about some open problems and challenges that need to be paid more attention in future research.

Network Structure Inference, A Survey: Motivations, Methods, and Applications

Networks are used to represent relationships between entities in many complex systems, spanning from online social networks to biological cell development and brain activity. These networks model relationships which present various challenges. In many cases, relationships between entities are unambiguously known: are two users friends in a social network? Do two researchers collaborate on a published paper? Do two road segments in a transportation system intersect? These are unambiguous and directly observable in the system in question. In most cases, relationship between nodes are not directly observable and must be inferred: does one gene regulate the expression of another? Do two animals who physically co-locate have a social bond? Who infected whom in a disease outbreak? Existing approaches use specialized knowledge in different home domains to infer and measure the goodness of inferred network for a specific task. However, current research lacks a rigorous validation framework which employs standard statistical validation. In this survey, we examine how network representations are learned from non-network data, the variety of questions and tasks on these data over several domains, and validation strategies for measuring the inferred network's capability of answering questions on the original system of interest.

Approximate Communication: Techniques for Reducing Communication Bottlenecks in Large-Scale Parallel Systems

Approximate computing has gained research attention recently as a way to increase energy efficiency and/or performance by exploiting some applications' intrinsic error resiliency. However, little attention has been given to its potential for tackling the communication bottleneck which remains as one of the looming challenges to be tackled for efficient parallelism. This paper seeks to explore the potential benefits of approximate computing for communication reduction by surveying four promising techniques for approximate communication - compression, relaxed synchronization, value prediction, and accelerators. The techniques are compared based on an evaluation framework composed of: communication cost reduction, performance, energy reduction, application domain, overheads, and output degradation. Comparison results show that lossy link compression and approximate value prediction are good choices for reducing the communication bottleneck in bandwidth constrained applications, while relaxed synchronization and approximate accelerators can achieve greater speedups on applications amenable to these techniques. Finally, this paper also includes several suggestions for future research on approximate communication techniques.

Auto-scaling Web Applications in Clouds: A Taxonomy and Survey

Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to auto-scale the resources provisioned to their applications under dynamic workload in order to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this paper, we comprehensively analyze the challenges remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scaling systems according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weakness in this field. Moreover, based on the analysis, we propose new future directions.

A Survey on NoSQL Stores

Recent diversity of storage demands made various shortcomings of traditional RDBMS systems revealed, which in turn led to the emergence of a new trend of complementary non-relational data management solutions, named as NoSQL (Not only SQL). This survey mainly aims at presenting the work that has been conducted with regard to four closely related concepts of NoSQL stores: data model, consistency model, data partitioning and replication. For each concept, its different protocols, and for each protocol, its corresponding features, strengths and drawbacks are explained. Furthermore, various implementations of each protocol are exemplified and crystallized through a collection of representative academic and industrial NoSQL technologies. The rationale behind each design decision along with some corresponding extensions and improvements are discussed. Finally, we disclose some existing challenges in developing effective NoSQL stores, which need attention from the research community, application designers and architects.

Large-Scale Indexing, Discovery and Ranking for the Internet of Things (IoT)

Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. Internet of Things (IoT) uses these network-enabled devices and communication technologies to allow connectivity and integration of physical objects (Things) from real-world into the data-driven digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data is, however, often multi-variant streams that are heterogeneous, sporadic, multi-modal and spatio-temporal. IoT data can be disseminated with different granularities and have diverse structures, types and qualities. Dealing with data deluge from heterogeneous IoT resources and services impose challenges on indexing, discovery and ranking mechanisms to build applications that require on-line access and retrieval of IoT data. However, the existing IoT data indexing and discovery approaches are complex (usually based on formal and logical methods) or centralised which hinder their scalability. The primary objective of this paper is to provide a holistic overview of the state-of-the-art on indexing, discovering and ranking of IoT data. We discuss on-line analysis and fast responses to complex queries. The paper aims to pave the way for researchers to design, develop, implement and evaluate techniques and approaches in future for on-line large-scale distributed IoT applications and platforms.

A Survey On Authenticated Encryption -- ASIC Designer's Perspective

Authenticated encryption (AE) has long been a vital operation in cryptography due to its ability to provide confidentiality, integrity and authenticity at the same time. Its use has soared in parallel with widespread use of Internet and has led to several new schemes. There have already been studies investigating software performance of various schemes. However, the same is yet to be done for hardware. In this paper, we present a comprehensive survey of hardware performance of the most commonly used authenticated encryption schemes in literature. These schemes include encrypt-then-MAC combination, block cipher based AE modes, relatively new authenticated encryption ciphers and the recently-introduced permutation-based AE scheme. For completeness, we implemented each scheme with various standardized block ciphers and/or hash algorithms, and their lightweight versions. In our evaluation, we targeted minimizing the time-area product while maximizing the throughput on ASIC platforms. 45nm NANGATE Open Cell Library was used for syntheses. In the results, we present area, speed, time-area product, throughput, and power figures for both standard and lightweight versions of each scheme. Finally, we provide an unbiased discussion on the impact of the structure and complexity of each scheme on hardware implementation, together with recommendations on hardware-friendly authenticated encryption scheme design.

Analysis and Classification of Shape-Changing Interfaces for Design and Application-based Research

Shape-changing interfaces are physically tangible, interactive devices, surfaces or spaces. Over the last fifteen years, research has produced functional prototypes over many use-applications, and reviews have identified themes and possible future directions  but have not yet looked at possible design or application based research. Here we gather this information together to provide a reference for designers and researchers wishing to build upon existing prototyping work, using synthesis and discussion of existing shape-changing interface reviews and comprehensive analysis and classification of 78 shape-changing interfaces. Eight categories of prototype are identified, alongside recommendations for the field.

A survey of techniques for automatically sensing the behavior of a crowd

Crowd-centric research is receiving increasingly more attention as data sets on crowd behavior are becoming readily available. We have come to a point that many of the models on pedestrian analytics introduced in the last decade, which have mostly not been validated, can now be tested using real-world data sets. In this survey we concentrate exclusively on automatically gathering such data sets, which we refer to as sensing the behavior of pedestrians. We roughly distinguish two approaches: one that requires users to explicitly use local applications and wearables, and one that scans the presence of handheld devices such as smartphones. We come to the conclusion that despite the numerous reports in popular media, relatively few groups have been looking into practical solutions for sensing pedestrian behavior. Moreover, we find that much work is still needed, in particular when it comes to combing privacy, transparency, scalability, and ease of deployment. We report on over 90 relevant articles and discuss and compare in detail 30 reports on sensing pedestrian behavior.

Geomagnetism for Smartphone-Based Indoor Localization: Challenges, Advances, and Comparisons

Geomagnetism has recently attracted considerable attention for indoor localization due to its pervasiveness and unreliance on extra infrastructure. Its location signature has been observed to be temporally stable and spatially discernible for localization purposes. This survey investigates the recent challenges and advances in geomagnetism-based indoor localization using smartphones. We first study smartphone-based geomagnetism measurements. We then review recent efforts in database construction and computation reduction, followed by state-of-the-art schemes in localizing the target. For each category, we identify practical deployment challenges and compare related studies. Finally, we summarize future directions and provide guideline for new researchers in this field.

Survey on Access Control for Community-Centered Collaborative Systems

The last decades have seen a growing interest and demand for collaborative systems and platforms. These systems and platforms aim to provide an environment in which users can collaboratively create, share and manage resources. While offering attractive opportunities for online collaboration and information sharing, they also open several security and privacy issues. This has attracted several research efforts towards the design and implementation of novel access control solutions that can handle the complexity introduced by collaboration. Despite these efforts, transition to practice has been hindered by the lack of maturity of the proposed solutions. The access control solutions typically adopted by commercial collaborative systems like online social network websites and collaborative editing platforms, are still rather rudimentary and do not provide users with a sufficient control over their resources. This survey examines the growing literature on access control for collaborative systems centered on communities, and identifies the main challenges to be addressed in order to facilitate the adoption of collaborative access control solutions in real-life settings. Based on the literature study, we delineate a roadmap for future research in the area of access control for community-centered collaborative systems.

Objectives and State-of-the-Art of Location-based Social Network Recommender Systems

Owing to the widespread adoption of GPS-enabled devices, such as smart phones and GPS navigation devices, more and more location information is being collected. Compared with traditional ones (e.g., Amazon, Taobao and Dangdang), recommender systems built on location-based social networks (LBSNs) have received much attention. The former mine users preference through the relationship between users and items, e.g., online commodity, movies and music. Based on their preference, items in which they may be interested are recommended in order to help them find the items that they may like. The latter add location as a new dimension to the former, hence resulting in the three-dimensional relationship among users, locations and activities. Based on this relationship, locations, activities and friends can be recommended to users. For example, users are allowed to check in at different location on Facebook and Foursquare by using their GPS-enabled devices, which can be further used to analyze their preference. In the paper, we review the objectives and state-of-the-art of LBSN recommender systems. We indicate potential research directions.

Automatic Software Repair: a Bibliography

This article presents an annotated bibliography on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs, without human intervention. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages and security. Furthermore, it provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature.

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing traditional scientific applications and analytics business services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from dedicated on-premise environments to shared public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make it easier its usage. Moreover, the discussion on the right pricing and contractual models that will fit both small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.

Facial Expression Analysis under Partial Occlusion: A Survey

Automatic machine-based Facial Expression Analysis (FEA) has witnessed substantial progress in the past few decades motivated by its importance in psychology, security, health, entertainment and human computer interaction. However, the vast majority of current studies are based on non-occluded faces collected in a controlled laboratory environment, and automatic expression recognition from partially occluded faces remains a largely unresolved field, particularly in real-world scenarios. In recent years, increasing efforts have been directed at investigating techniques to handle partial occlusion for FEA. This survey provides a comprehensive review of the recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion, which are crucial in system design and evaluations. It also outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and devoted to serve as a starting point to promote future work.

Towards Reasoning Vehicles: A Survey of Fuzzy Logic-based Solutions in Vehicular Networks

Vehicular networks and their associated technologies enable an extremely varied plethora of applications and therefore attract increasing attention from a wide audience. However vehicular networks also have many challenges that arise mainly due to their dynamic and complex environment. Fuzzy Logic, known for its ability to deal with complexity, imprecision and model non-deterministic problems, is a very promising technology for use in such a dynamic and complex context. This paper presents the first comprehensive survey of research on Fuzzy Logic approaches in the context of vehicular networks, and provides fundamental information which enables readers to design their own Fuzzy Logic systems in this context. As such, the paper describes the Fuzzy Logic concepts with emphasis on their implementation in vehicular networks, includes a classification and thorough analysis of the Fuzzy Logic-based solutions in vehicular networks and discusses how Fuzzy Logic could empower the key research directions in the 5G-enabled vehicular networks, the next generation of vehicular communications.

Metamorphic Testing: A Review of Challenges and Opportunities

Metamorphic testing is an approach to both test case generation and test result verification. A central element is a set of metamorphic relations, which are necessary properties of the target function or algorithm in relation to multiple inputs and their expected outputs. Since its first publication, we have witnessed a rapidly increasing body of work examining metamorphic testing from various perspectives, including metamorphic relation identification, test case generation, integration with other software engineering techniques, and the validation and evaluation of software systems. In this paper, we review the current research of metamorphic testing and discuss the challenges yet to be addressed. We also present visions for further improvement of metamorphic testing and highlight opportunities for new research.

A Checkpoint of Research on Parallel I/O for High Performance Computing

This article presents a comprehensive survey on parallel I/O. This is an important field for High Performance Computing because of the historic gap between processing power and storage latencies, which causes applications performance to be impaired when accessing or generating large amounts of data. As the available processing power and amount of data increase, I/O remains a central issue for the scientific community. In this survey, we present background concepts everyone could benefit from. Moreover, through the comprehensive study of publications from the most important conferences and journals in a five-year time window, we discuss the state of the art of I/O optimization approaches, access pattern extraction techniques, and performance modeling, in addition to general aspects of parallel I/O research. Through this approach, we aim at identifying the general characteristics of the field and the main current and future research topics.

On Fault Detection and Diagnosis in Robotic Systems

Robots are sophisticated machines that are susceptible to different types of faults. These faults have to be detected and diagnosed in time to allow recovery and continuous operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. Yet, the study of FDD for robotics is relatively new, and only few surveys were presented. These surveys have focused on traditional FDD approaches and how they may broadly apply to a generic type of robots. Yet, robotic systems can be identified by fundamental characteristics, which pose different constraints and requirements from FDD. In this paper, we aim to provide the reader with useful insights regarding the use of FDD approaches which best suit the different characteristics of robotic systems. We elaborate on the advantages and the challenges these approaches must face. We use two perspectives: (1) FDD from the perspective of the different characteristics of robotic systems, and (2) FDD from the perspective of the different approaches. Finally, we describe research opportunities. With these three contributions readers from both the FDD and the robotics research communities are introduced to this subject.

A Comprehensive Taxonomy of Fragmentation and Allocation Techniques in Distributed Database Design

Designing an optimal distributed database is an extremely complex process due to many factors like large number of relations, data transmission costs, number of network sites, communication costs between sites and query response time. In the sake of achieving an optimal design, fragmentation, replication and data allocation techniques are the key factors for providing a high rendering and supporting data access and sharing at different sites. It is worth saying, however, that these techniques often treated separately and rarely processed together. Some researches sought to find only optimal allocation methods regardless of how the fragmentation technique is performed or replication process is adopted. In contrast, others attempt to find the best fragment solution without considering how allocation would be performed. In this paper, most of different fragmentation, replication and allocation techniques are extensively and precisely scrutinized in contemporary literature for both centralized and distributed databases. Furthermore, some of these techniques presented as cases study for well-analyzed fragmentation and allocation models. These cases are cited as evidence proving that a well designed distributed database can result in significant reduction in communication costs, response time and substantial boost in performance outperforming over centralized systems for geographically distributed sites.

Edge-Oriented Computing Paradigms: A Survey on Architecture Design and System Management

While cloud computing has brought paradigm shifts to computing services, researchers and developers have also found some problems inherent to its nature such as bandwidth bottleneck, communication overhead, and location blindness. The concept of fog/edge computing is therefore coined to extend the services from the core in cloud data centers to the edge of the network. In recent years, many systems are proposed to better serve ubiquitous smart devices closer to the user. This paper provides a complete and up-to-date review of edge-oriented computing systems by encapsulating relevant proposals on their architecture features, management approaches, and design objectives.

Detection and Resolution of Rumours in Social Media: A Survey

Despite the increasing use of social media for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e. unverified pieces of information. At the same time, the openness of social media provides opportunities to study how users share and discuss rumours, and to explore how natural language processing and data mining techniques may be used to find ways of determining their veracity. In this survey we introduce and discuss two types of rumours that circulate on social media; long-standing rumours that circulate for long periods of time, and newly-emerging rumours spawned during fast-paced events such as breaking news, where unverified reports are often released piecemeal. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these components. We summarise the efforts and achievements so far towards the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for detection and resolution of rumours.

Activity Recognition with Evolving Data Streams: A Review

Activity recognition aims to provide accurate and opportune information on peoples activities by leveraging sensory data available in todays sensory rich environments. Nowadays, activity recognition has become an emerging field in the areas of pervasive and ubiquitous computing. A typical activity recognition technique processes data streams that evolve from sensing platforms such as mobile sensors, on body sensors, and/or ambient sensors. This paper surveys the two overlapped areas of research of activity recognition and data stream mining. The perspective of this paper is to review the adaptation capabilities of activity recognition techniques in streaming environment. Broad categories of techniques are identified based on the different features in both data streams and activity recognition. The pros and cons of the algorithms in each category are analysed and the possible directions of future research are indicated.

A Survey on the Usage of Eye-Tracking in Computer Programming

In recent years, eye-tracking has been used by researchers in the field of programming education to analyse and understand tasks such as code comprehension, debugging, collaborative programming, tractability and the comprehension of non-code programming representations. Eye-trackers are used to gain more insights into the cognitive process of programmers and programming techniques. In this paper, we perform a systematic literature review (SLR) on existing research using eye-tracking in computer programming. We identify, evaluate, and report 65 studies, published between 1990 and 2015. Participants in these studies were mainly students and faculty members with the common programming language used are Java and UML representation. We also report on a range of eye-trackers and attention tracking tools utilized in these studies and found that the Tobii eye-trackers are more preferred among researchers. In this SLR, we report the findings based on the materials, participant sample, and eye-tracking device used in each experiment.

A Survey on Online Judge Systems and Their Applications

Online judges are systems designed for the reliable evaluation of algorithm source code submitted by users, which is next compiled and tested in a homogeneous environment. Online judges are becoming popular in various applications. Thus, we would like to review the state of the art for these systems. We classify them according to their principal objectives into systems supporting organization of competitive programming contests, enhancing education and recruitment processes, or facilitating the solving of data mining challenges, online compilers and development platforms integrated as components of other custom systems. Moreover, we present the platform, which has been proposed for the solving of complex optimization problems. We also present the advantages of our system by analysis of the competition results conducted using the proposed platform. The competition proved that this platform, strengthened by crowdsourcing concepts, can be successfully applied to accurately and efficiently solve complex industrial- and science-driven challenges.

Multimedia Big Data Analytics: A Survey

With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. Lots of research work have been done in the multimedia area, targeting at different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey which targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.

Multi-Robot Assembly Strategies and Metrics

We present a survey of multi-robot assembly applications and methods, and describe trends and general insights into the multi-robot assembly problem for industrial applications. We focus on fixtureless assembly strategies featuring two or more robotic systems. Such robotic systems include industrial robot arms, dexterous robotic hands, and autonomous mobile platforms, such as automated guided vehicles. In this survey, we identify the types of assemblies that are enabled by utilizing multiple robots, the algorithms that synchronize the motions of the robots to complete the assembly operations, and the metrics used to assess the quality and performance of the assemblies.

Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions

Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar  all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions.

Are we ready to tackle Software Defined Networks? A Comprehensive Survey on Management Tools and Techniques

Network management and maintenance are time-consuming and often challenging tasks. With the emergent Software-Defined Networking paradigm, most of the focus is directed to the evolution of control protocols and platforms, or to deployment problems. Although researchers and network operators consider network management as a primary requirement, its development in SDN has been apparently set aside. This paper reports on the SDN architecture, introduces the concept of SDN tools and surveys the state-of-the-art in different aspects of the network management with emphasis on SDN. Because the SDN ecosystem lacks of a standardized management framework, initiatives are different and scattered.

Data-driven Approaches to Game Player Modeling: A Systematic Literature Review

Context: Recent years have seen growing interest in open-ended interactive tools such as games. One of the most crucial factors in developing games is to model and predict individual behavior. Although model-based approaches have been considered a standard way for this purpose, their application is often extremely difficult due to a huge space of actions can be created by games. For this reason, data-driven approaches have shown promise, in part because they are not completely reliant on expert knowledge. Objective: This study seeks to systematically review the existing research on the use of data-driven approaches in game player modeling. Method: We have carefully surveyed a nine-year sample (2008-2016) of experimental studies conducted on data-driven approaches in game player modeling, and thereby found 36 studies that addressed four primary research questions, and so we analyzed and classified the questions, methods, and findings of these published works, which we evaluated and drew conclusions from based on non-statistical methods. Results: We found that there are three primary avenues in which data-driven approaches have been studied in games research. In conclusion, we highlight critical future challenges in the area and offer directions for future study

A Systematic Review of Cloud Modeling Languages

Modern cloud environments support a relatively high degree of automation in service provisioning, which allows cloud users to dynamically acquire services required for deploying cloud applications. Cloud modeling languages (CMLs) have been proposed to address the diversity of features provided by todays cloud environments and support different application scenarios, e.g. migrating existing applications to the cloud, developing new cloud applications, or optimizing them. There is, however, still much debate on what a CML is and what aspects of a cloud application and the target cloud environment should be modeled by a CML. Furthermore, the distinction between CMLs on a fine-grained level exposing their modeling concepts is rarely made. In this article, we investigate the diverse features currently provided by existing CMLs. We classify and compare them according to a common framework with the goal to support cloud users in selecting the CML which fits the needs of their application scenario and setting. As a result, not only features of existing CMLs are pointed out for which extensive support is already provided but also in which existing CMLs are deficient, thereby suggesting a research agenda for the future.


Publication Years 1969-2017
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Joel Emer ACM-IEEE CS Eckert-Mauchly Award (2009)
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Thomas G Price ACM Programming Systems and Languages Paper Award (1982)
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Azriel Rosenfeld ACM Fellows (1994)
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Daniela Rus ACM Fellows (2014)
Barbara Gershon Ryder ACM Presidential Award (2008)
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Sherif Sakr ACM Senior Member (2017)
Ahmed Sameh ACM Fellows (1999)
Hanan Samet ACM Paris Kanellakis Theory and Practice Award (2011)
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Ravinderpal S Sandhu ACM Fellows (2001)
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Mahadev Satyanarayanan ACM Software System Award (2016)
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John E Savage ACM Fellows (1996)
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William L Scherlis ACM Senior Member (2009)
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Robert Sedgewick ACM Fellows (1997)
Bart Selman ACM Fellows (2012)
Lui Sha ACM Fellows (2005)
Marc Shapiro ACM Senior Member (2009)
Micha Sharir ACM Fellows (1997)
Dennis E Shasha ACM Fellows (2013)
Alan C Shaw ACM Fellows (1997)
Mary M Shaw ACM Fellows (1996)
Prashant J Shenoy ACM Distinguished Member (2009)
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Kang Shin ACM Fellows (2001)
Frank Shipman ACM Distinguished Member (2009)
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Yoav Shoham ACM AAAI Allen Newell Award (2012)
ACM Fellows (2012)
Howard Siegel ACM Fellows (1998)
Joseph Sifakis ACM Fellows (2012)
ACM A. M. Turing Award (2007)
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ACM Fellows (1996)
Kevin Skadron ACM Fellows (2015)
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Jacob Slonim ACM Doctoral Dissertation Award (1980)
Alan Smith ACM Fellows (2001)
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Diomidis Spinellis ACM Senior Member (2006)
Richard Stallman ACM Software System Award (2015)
ACM Grace Murray Hopper Award (1990)
John A Stankovic ACM Fellows (1996)
John Stasko ACM Distinguished Member (2011)
ACM Senior Member (2011)
Giancarlo Succi ACM Senior Member (2008)
S. Sudarshan ACM Fellows (2014)
Subhash Suri ACM Fellows (2010)
ACM Distinguished Member (2007)
Ivan Sutherland ACM Fellows (1994)
ACM Software System Award (1993)
ACM A. M. Turing Award (1988)
Jeffrey Victor Sutherland ACM Senior Member (2009)
Roberto Tamassia ACM Fellows (2012)
Andrew S Tanenbaum ACM Fellows (1996)
Andrew S. Tanenbaum ACM Karl V. Karlstrom Outstanding Educator Award (1994)
Robert W Taylor ACM Fellows (1994)
ACM Software System Award (1984)
Bent Thomsen ACM Senior Member (2016)
Irv Traiger ACM Fellows (1994)
ACM Software System Award (1988)
ACM Programming Systems and Languages Paper Award (1982)
ACM Programming Systems and Languages Paper Award (1971)
Allen Tucker ACM Fellows (1994)
Outstanding Contribution to ACM Award (1991)
Jeffrey D Ullman ACM Karl V. Karlstrom Outstanding Educator Award (1997)
ACM Fellows (1995)
Patrick Valduriez ACM Fellows (2012)
Mateo Valero ACM Distinguished Service Award (2012)
ACM-IEEE CS Eckert-Mauchly Award (2007)
ACM Fellows (2002)
Paul Van Oorschot ACM Fellows (2016)
Robbert Van Renesse ACM Fellows (2009)
C J Van Rijsbergen ACM Fellows (2003)
Moshe Y Vardi ACM Presidential Award (2017)
Outstanding Contribution to ACM Award (2009)
ACM Presidential Award (2008)
ACM Paris Kanellakis Theory and Practice Award (2005)
ACM Fellows (2000)
Sebastian Ventura ACM Senior Member (2013)
Jeffrey S Vetter ACM Distinguished Member (2012)
ACM Gordon Bell Prize
Performance (2010)
Philip Wadler ACM Fellows (2007)
David L Waltz ACM Fellows (1999)
Yu Wang ACM Senior Member (2011)
Anthony I Wasserman ACM Fellows (1996)
Peter Wegner ACM Distinguished Service Award (2000)
ACM Fellows (1995)
Bruce W Weide ACM Senior Member (2009)
Fred W Weingarten ACM Fellows (1996)
Edgar Weippl ACM Senior Member (2008)
Michael Wellman ACM Fellows (2005)
John R. White ACM Presidential Award (2015)
ACM Fellows (1995)
Outstanding Contribution to ACM Award (1994)
Jennifer Widom ACM Athena Lecturer Award (2015)
ACM Fellows (2005)
Gio Wiederhold ACM Fellows (1995)
Reinhard Wilhelm ACM Distinguished Service Award (2010)
ACM Fellows (2000)
John Wilkes ACM Fellows (2002)
Maurice V. Wilkes ACM Fellows (1994)
ACM-IEEE CS Eckert-Mauchly Award (1980)
ACM A. M. Turing Award (1967)
Jeannette M. Wing ACM Distinguished Service Award (2014)
ACM Fellows (1998)
Niklaus E Wirth ACM Fellows (1994)
ACM A. M. Turing Award (1984)
ACM Programming Systems and Languages Paper Award (1978)
Ian Witten ACM Fellows (1996)
Ouri Wolfson ACM Fellows (2001)
Pierre Wolper ACM Paris Kanellakis Theory and Practice Award (2005)
Jie Wu ACM Senior Member (2006)
William A Wulf ACM Karl V. Karlstrom Outstanding Educator Award (2014)
ACM Distinguished Service Award (2011)
ACM Fellows (1994)
Qiang Yang ACM Distinguished Member (2011)
Bulent Yener ACM Senior Member (2013)
Philip S Yu ACM Fellows (1997)
Pamela Zave ACM Fellows (2001)
HongJiang Zhang ACM Fellows (2007)
Lixia Zhang ACM Fellows (2006)
Feng Zhao ACM Distinguished Member (2006)
Yuanyuan Zhou ACM Fellows (2013)
ACM Distinguished Member (2011)
Albert Zomaya ACM Distinguished Member (2006)
Stuart Zweben ACM Fellows (1998)
Outstanding Contribution to ACM Award (1997)
Andries van Dam ACM Fellows (1994)
ACM Karl V. Karlstrom Outstanding Educator Award (1993)
Andries van Dam ACM Fellows (1994)
ACM Karl V. Karlstrom Outstanding Educator Award (1993)

First Name Last Name Paper Counts
Peter Denning 21
Adele Goldberg 18
William Dorn 9
Anthony Wasserman 8
Sparsh Mittal 7
Helen Ashman 6
Rachid Guerraoui 6
Andries Van Dam 6
Rajkumar Buyya 6
Peter Wegner 6
Jeffrey Vitter 5
Rance Cleaveland 5
Yannis Ioannidis 5
Jacques Cohen 5
Elliott Organick 5
Andrew Tanenbaum 5
Wendy Hall 4
Corrado Priami 4
Gonzalo Navarro 4
Albert Zomaya 4
James Fry 4
James Peterson 4
Pierpaolo Degano 4
Peter Lee 4
Fred Schneider 4
Jayadev Misra 4
Peter Wegner 4
Sameeullah Khan 4
Azzedine Boukerche 4
Azriel Rosenfeld 4
Richard Muntz 4
Hanan Samet 4
Jens Palsberg 4
Narendra Ahuja 4
Brad Myers 4
Tamer Özsu 4
Sundaraja Iyengar 4
Gregory Andrews 4
Jeffrey Buzen 4
Luca Cardelli 4
Scott Smolka 4
Mohamed Fayad 4
Johnleslie King 3
Bruce Weide 3
Toby Teorey 3
Miroslaw Malek 3
David Schmidt 3
Robert Rosin 3
Pankaj Agarwal 3
Nabil Adam 3
Charles Consel 3
Abdullah Gani 3
Ingrid Carlbom 3
David Lilja 3
Avi Silberschatz 3
Dick Bulterman 3
Leslie Carr 3
Michael Bieber 3
Goetz Graefe 3
Robyn Kozierok 3
Mark Harman 3
Cristina Nita-Rotaru 3
Werner Retschitzegger 3
Chris Hankin 3
Alberto Pettorossi 3
Patrick Cousot 3
Jonathan Grudin 3
Thomas Dean 3
Fabrício Enembreck 3
Maurizio Proietti 3
John Rice 3
Michael, Loui 3
John Stankovic 3
Quan Sheng 3
Emil Lupu 3
Marvin Zelkowitz 3
Udo Pooch 3
Mahadevan Ganapathi 3
Garth Gibson 3
Raghu Ramakrishnan 3
Dirk Riehle 3
Jon Doyle 3
George Nagy 3
Alexander Thomasian 3
Laurie Damianos 3
Zimu Zhou 3
Carlo Furia 3
Francky Catthoor 3
Henry Ledgard 3
Hanne Nielson 3
Flemming Nielson 3
Roberto Tamassia 3
Randy Katz 3
Frank Manola 3
Jacques Noyé 3
Ben Shneiderman 3
Isabel Cruz 3
Roberto Gorrieri 3
David De Roure 3
Michael Marcotty 3
Joseph Pasquale 3
Michael Mannino 2
Richard Snodgrass 2
Ahmed Elmagarmid 2
Gilles Muller 2
Tiziana Catarci 2
Kenneth Neves 2
John Mitchell 2
Philip Bernstein 2
Alexander Brodsky 2
Donald Knuth 2
Ann Fitzsimmons 2
Tom Love 2
Erik Sandewall 2
Fabio Vitali 2
Daniel Schwabe 2
Narain Gehani 2
Dale Miller 2
Ivona Brandić 2
Kim Bruce 2
Jeffrey Kurtz 2
Edmund Clarke 2
Clement Yu 2
Aristides Requicha 2
Zvi Galil* 2
David Maier 2
Guillaume Pierre 2
Pieter Hartel 2
William Stallings 2
Carlo Batini 2
Alberto Marchetti-Spaccamela 2
Philip YU 2
Jan Heering 2
Philippe Codognet 2
Mary Rosson 2
Betty Salzberg 2
Henry Korth 2
Dino Mandrioli 2
Renaud Marlet 2
Nic Volanschi 2
Ouri Wolfson 2
Michael Leuschel 2
Martha Larson 2
Ephraim Glinert 2
Tiziana Margaria 2
Patricia Lago 2
CÉcile Paris 2
Yue Huang 2
Wubai Zhou 2
Robbert Van Renesse 2
Peter Larsen 2
Marjan Mernik 2
Mark Truran 2
David Kotz 2
Zhiwen Yu 2
Thierry Lecroq 2
Sherif Sakr 2
Hector Levesque 2
Claudio Carpineto 2
Richard Bird 2
Giuseppe Italiano 2
Mohammed Bennamoun 2
Jim Woodcock 2
Jim Kurose 2
Michael Hanus 2
Fred Maryanski 2
Edward Reingold 2
William Wulf 2
Charles Fischer 2
Antônio Loureiro 2
Chittoor Ramamoorthy 2
Eduardo Hruschka 2
Angelo Morzenti 2
Robert Sedgewick 2
David Embley 2
Maarten Van Steen 2
Benjamin Bustos 2
Daniel Keim 2
André Schiper 2
Gerti Kappel 2
Kevin Crowston 2
John Hennessy 2
Eduardo Miranda 2
Philip Wadler 2
William Kent 2
Carl Landwehr 2
Hans Gellersen 2
Abdelhamid Bouchachia 2
Renzo Angles 2
Peter Fontana 2
Frederick Lochovsky 2
Won Kim 2
Wolfgang Klas 2
Lester Lipsky 2
Josep Silva 2
Thomas Moran 2
Pascal Felber 2
Subrata Dasgupta 2
Subhash Suri 2
James Michener 2
Paul Hudak 2
Lawrence Dowdy 2
Derrell Foster 2
João Gama 2
Charles Hoare 2
Juris Hartmanis 2
Howard Wactlar 2
Kevin Mills 2
John Gallagher 2
Douglas Schmidt 2
Leon Osterweil 2
Steve Bryson 2
Francis Sullivan 2
Vijay Saraswat 2
Nathan Goodman 2
Joseph Sifakis 2
Robert Harper 2
Bernhard Steffen 2
Ugo Montanari 2
Brian Davison 2
William Enck 2
Rizos Sakellariou 2
Sungho Kang 2
Zhiqiang Lin 2
Tomáš Skopal 2
Thomas Dietterich 2
Yonathan Bard 2
Alistair Moffat 2
Klara Nahrstedt 2
Kenneth Kraemer 2
Gary Leavens 2
Ricardo Baeza-Yates 2
Cinzia Cappiello 2
Dana Angluin 2
Leon Presser 2
Norman Meyrowitz 2
H Li 2
James Foley 2
Fatima Nunes 2
Yexi Jiang 2
Umesh Bellur 2
Mohamed Sabry 2
David Atienza 2
John Derrick 2
Otto Lessing 2
Sigmund Gorski 2
Carl Gunter 2
Arun Ross 2
Victor Lesser 2
Douglas Comer 2
Malvin Kalos 2
Pierre Wolper 2
Tom Van Cutsem 2
Andrzej Jajszczyk 2
Michael Franz 2
Chunqiu Zeng 2
Raymond Yeh 2
Christos Faloutsos 2
Albert Bifet 2
Imran Ghani 2
Sitharama Iyengar 2
Inderveer Chana 2
Andreas Bulling 2
Sivan Toledo 2
Alexander Gluhak 2
Axel Jantsch 2
Andrew Newell 2
Ilia Pietri 2
Justin Zobel 2
Giovanni Romano 2
Natarajan Shankar 2
Bruce Leverett 2
Olivier Danvy 2
Eduardofreire Nakamura 2
Dennis Volpano 2
Barbara Ryder 2
Alan Feuer 2
Matthias Jarke 2
Sushil Jajodia 2
Andy Cockburn 2
Julia Lawall 2
Mehmet Akşit 2
John Wilkes 2
Pascal Van Hentenryck 2
George Cybenko 2
Yale Patt 2
Chunyan Miao 2
Sarvnaz Karimi 2
Kotagiri Ramamohanarao 2
Jones Granatyr 2
Jean Hubaux 2
Levente Buttyán 2
Maxime Crochemore 2
Charu Aggarwal 2
John Roddick 2
Sabri Mahmoud 2
Ronald Brachman 2
Michael Wellman 2
Gary Sockut 2
Siba Mohanty 2
Patrick Hall 2
Frank Piessens 2
Jeffrey Ullman 2
Lynette Hirschman 2
Edgar Sibley 2
John Hatcliff 2
Athanasios Vasilakos 2
Allen Tucker 2
Mathias Fischer 2
Shishir Nagaraja 2
Dimitrios Rodopoulos 2
Lionel Ni 2
Niki Pissinou 2
John Fitzgerald 2
Jeannette Wing 2
Krithi Ramamritham 2
Anne Kermarrec 2
Max Mühlhäuser 2
James Stanier 2
Edson Scalabrin 2
William Smyth 2
Niall Murray 2
Gabriel Muntean 2
Cornelia Fermüller 2
Catherine McGeoch 2
George Lueker 2
Jonathan Walpole 2
James Church 2
Robert Sproull 2
Asuman Dogac 2
Abraham Silberschatz 2
C Hoare 2
Rosemary Simpson 2
Daniel Le Métayer 2
Philip Treleaven 2
Richard Millar 2
Alan Burns 2
David Brailsford 2
Fabio Crestani 2
Stephen Taylor 2
Karl Lieberherr 2
Kevin Martin 2
Scott Thibault 2
Laura Lafave 2
Davide Brugali 2
Brian Randell 2
Lori Clarke 2
Peter Triantafillou 2
Hafedh Mili 2
Georgia Psychou 2
Tobias Noll 2
Matteo Rossi 2
Boualem Benatallah 2
Serge Demeyer 2
Jochen Huber 2
Alípio Jorge 2
Giuseppe Anastasi 2
Simon Kasif 2
Elisha Sacks 2
Ronald Loui 2
Dennis Severance 2
Jürg Nievergelt 2
Joseph Paciorek 2
Andrew Turpin 2
Jörg Rothe 2
Bruce Schachter 2
Theodor Nelson 2
Harri Oinas-Kukkonen 2
Gustavo Rossi 2
Walter Kohler 2
Faron Moller 2
Robert Jacob 2
Luke Hornof 2
Katia Sycara 2
Sajjad Madani 2
Trevor Mudge 2
Wayne Citrin 2
James Hollan 2
Jacqueline Kory 1
Nidhi Tiwari 1
JongWon Kim 1
Maria Costabile 1
Yunhao Liu 1
Steffen Wendzel 1
Tom Dinkelaker 1
Joaquím Salví 1
Dimitrios Soudris 1
Sergiy Vilkomir 1
Alicia Iriberri 1
Matteo Pradella 1
Theodore Linden 1
Veljko Pejovic 1