ACM Computing Surveys

Journal: ACM Computing Surveys

Abbreviation

ACM comput. surv.

Publisher

Association for Computing Machinery

ISSN

0360-0300
1557-7341

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Publications 1 - 10 of 13

Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries
Besta, Maciej; Gerstenberger, Robert; Peter, Emanuel; et al. (2023)
ACM Computing Surveys
Numerous irregular graph datasets, for example social networks or web graphs, may contain even trillions of edges. Often, their structure changes over time and they have domain-specific rich data associated with vertices and edges. Graph database systems such as Neo4j enable storing, processing, and analyzing such large, evolving, and rich datasets. Due to the sheer size and irregularity of such datasets, these systems face unique design challenges. To facilitate the understanding of this emerging domain, we present the first survey and taxonomy of graph database systems. We focus on identifying and analyzing fundamental categories of these systems (e.g., document stores, tuple stores, native graph database systems, or object-oriented systems), the associated graph models (e.g., Resource Description Framework or Labeled Property Graph), data organization techniques (e.g., storing graph data in indexing structures or dividing data into records), and different aspects of data distribution and query execution (e.g., support for sharding and Atomicity, Consistency, Isolation, Durability). Fifty-one graph database systems are presented and compared, including Neo4j, OrientDB, and Virtuoso. We outline graph database queries and relationships with associated domains (NoSQL stores, graph streaming, and dynamic graph algorithms). Finally, we outline future research and engineering challenges related to graph databases.
Vision-Based Mobile App GUI Testing: A Survey
Yu, Shengcheng; Fang, Chunrong; Tuo, Ziyuan; et al. (2026)
ACM Computing Surveys
Graphical User Interface (GUI) has become one of the most significant parts of mobile applications (apps). It is a direct bridge between mobile apps and end users, which directly affects the end user’s experience. Neglecting GUI quality can undermine the value and effectiveness of the entire mobile app solution. Significant research efforts have been devoted to GUI testing, one effective method to ensure mobile app quality. By conducting rigorous GUI testing, developers can ensure that the visual and interactive elements of the mobile apps not only meet functional requirements but also provide a seamless and user-friendly experience. However, traditional solutions, relying on the source code or layout files, have met challenges in both effectiveness and efficiency due to the gap between what is obtained and what app GUI actually presents. Vision-based mobile app GUI testing approaches emerged with the development of computer vision technologies and have achieved promising progress. In this survey article, we provide a comprehensive investigation of the state-of-the-art techniques on 271 articles, among which 92 are vision-based studies. This survey covers different topics of GUI testing, like GUI test generation, GUI test record & replay, GUI testing framework, and so on. In particular, we highlight the emerging role of vision-based techniques and analyze how they reshape traditional approaches to mobile app GUI testing. Based on the investigation of existing studies, we outline the challenges and opportunities of (vision-based) mobile app GUI testing and propose promising research directions with the combination of emerging techniques.
A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations
Karimi, Amir-Hossein; Barthe, Gilles; Schölkopf, Bernhard; et al. (2023)
ACM Computing Surveys
Machine learning is increasingly used to inform decision making in sensitive situations where decisions have consequential effects on individuals’ lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role in the adoption and impact of said technologies. In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavorably treated by automated decision-making systems. We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. Then, we provide an overview of the prospective research directions toward which the community may engage, challenging existing assumptions and making explicit connections to other ethical challenges such as security, privacy, and fairness.
Wearable Activity Trackers: A Survey on Utility, Privacy, and Security
Salehzadeh Niksirat, Kavous; Velykoivanenko, Lev; Zufferey, Noé; et al. (2024)
ACM Computing Surveys
Over the past decade, wearable activity trackers (WATs) have become increasingly popular. However, despite many research studies in different fields (such as psychology, health, and design), few have sought to jointly examine the critical aspects of utility (i.e., benefits brought by these devices), privacy, and security (i.e., risks and vulnerabilities associated with them). To fill this gap, we reviewed 236 studies that researched the benefits of using WATs, the implications for the privacy of users of WATs, and the security vulnerabilities of these devices. Our survey revealed that these devices expose users to several threats. For example, WAT data can be mined to infer private information, such as the personality traits of the user. Whereas many works propose empirical findings about users’ privacy perceptions and their behaviors in relation to privacy, we found relatively few studies researching technologies to better protect users’ privacy with these devices. This survey contributes to systematizing knowledge on the utility, privacy, and security of WATs, shedding light on the state-of-the-art approaches with these devices, and discussing open research opportunities.
Security of Distance-Bounding: A Survey
Avoine, Gildas; Bingöl, Muhammed Ali; Boureanu, Ioana; et al. (2018)
ACM Computing Surveys
A Tutorial on Human Activity Recognition Using Body-Worn Inertial Sensors
Bulling, Andreas; Blanke, Ulf; Schiele, Bernt (2014)
ACM Computing Surveys
On Physical-layer Identification of Wireless Devices
Danev, Boris; Zanetti, Davide; Capkun, Srdjan (2012)
ACM Computing Surveys
A Survey on Automated Log Analysis for Reliability Engineering
He, Shilin; He, Pinjia; Chen, Zhuangbin; et al. (2021)
ACM Computing Surveys
Logs are semi-structured text generated by logging statements in software source code. In recent decades, software logs have become imperative in the reliability assurance mechanism of many software systems, because they are often the only data available that record software runtime information. As modern software is evolving into a large scale, the volume of logs has increased rapidly. To enable effective and efficient usage of modern software logs in reliability engineering, a number of studies have been conducted on automated log analysis. This survey presents a detailed overview of automated log analysis research, including how to automate and assist the writing of logging statements, how to compress logs, how to parse logs into structured event templates, and how to employ logs to detect anomalies, predict failures, and facilitate diagnosis. Additionally, we survey work that releases open-source toolkits and datasets. Based on the discussion of the recent advances, we present several promising future directions toward real-world and next-generation automated log analysis.
Modeling Time in Computing: A Taxonomy and a Comparative Survey
Furia, Carlo A.; Mandrioli, Dino; Morzenti, Angelo; et al. (2010)
ACM Computing Surveys
The increasing relevance of areas such as real-time and embedded systems, pervasive computing, hybrid systems control, and biological and social systems modeling is bringing a growing attention to the temporal aspects of computing, not only in the computer science domain, but also in more traditional fields of engineering. This article surveys various approaches to the formal modeling and analysis of the temporal features of computer-based systems, with a level of detail that is also suitable for nonspecialists. In doing so, it provides a unifying framework, rather than just a comprehensive list of formalisms. The article first lays out some key dimensions along which the various formalisms can be evaluated and compared. Then, a significant sample of formalisms for time modeling in computing are presented and discussed according to these dimensions. The adopted perspective is, to some extent, historical, going from “traditional” models and formalisms to more modern ones.
A Review of Error Estimation in Adaptive Quadrature
Gonnet, Pedro (2012)
ACM Computing Surveys

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