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215. Internet Technology Outlook

Author

M. Tamer Özsu and Latifur Khan

Subjects
Multimedia, Computer Networks and Communications, business.industry, Computer science, Cognitive computing, The Internet, business, computer.software_genre, computer
Published
2020
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221. Enabling Technology for Distributed Multimedia Applications.

Author

Johnny W. Wong, Kelly A. Lyons, David Evans 0003, Rolf J. Velthuys, Gregor von Bochmann, Eric Dubois 0002, Nicolas D. Georganas, Gerald W. Neufeld, M. Tamer özsu, Jeff Brinskelle, Abdelhakim Hafid, Norman C. Hutchinson, Paul Iglinski, Brigitte Kerhervé, Louise Lamont, Dwight J. Makaroff, and Duane Szafron

Published
1997
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234. The future is big graphs: a Community View on Graph Processing Systems

Author

Jan Hidders, Mohamed Ragab, Angela Bonifati, Hannes Voigt, Maciej Besta, Alexandru Iosup, Ana Lucia Varbanescu, Petra Selmer, Tim Hegeman, Joshua Shinavier, Katja Hose, Stefania Dumbrava, Vasiliki Kalavri, Matei Ripeanu, Gábor Szárnyas, Eiko Yoneki, Da Yan, Semih Salihoglu, Renzo Angles, Eric Peukert, Olaf Hartig, Christian Schulz, Marcelo Arenas, Riccardo Tommasini, Nikolay Yakovets, Alexandru Uta, Stefan Plantikow, Juan F. Sequeda, M. Tamer Özsu, Emanuele Della Valle, Peter Boncz, Khaled Ammar, Wim Martens, Hsiang-Yun Wu, Bernhard Haslhofer, Sherif Sakr, Adriana Iamnitchi, Khuzaima Daudjee, Walid G. Aref, Antonino Tumeo, Hugo Kapp, Institute of Computer Science [University of Tartu, Estonie], University of Tartu, Base de Données (BD), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Types and Reasoning for the Web (TYREX), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Neo4j, Vrije Universiteit Amsterdam [Amsterdam] (VU), Delft University of Technology (TU Delft), BorialisAI, Universidad de Talca, Purdue University [West Lafayette], UC Pontifical University Cathoic [Santiogo], Millennium Institute for Foundational Research on Data (IMFD), Pontificia Universidad Católica de Chile (UC), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Centrum Wiskunde & Informatica (CWI), University of Waterloo [Waterloo], Polytechnic University of Milan, Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE), Linköping University (LIU), Austrian Institute of Technology [Vienna] (AIT), Birkbeck College [University of London], Aalborg University [Denmark] (AAU), University of South Florida [Tampa] (USF), Boston University [Boston] (BU), Oracle Labs Switzerland, Universität Bayreuth, Universität Leipzig, University of British Columbia (UBC), Heidelberg University, data.world, Uber Engineering [Palo Alto], Budapest University of Technology and Economics [Budapest] (BME), Pacific Northwest National Laboratory (PNNL), University of Amsterdam [Amsterdam] (UvA), Institute of Applied Physics [Vienna] (TU Wien), Vienna University of Technology (TU Wien), Eindhoven University of Technology [Eindhoven] (TU/e), University of Alabama at Birmingham [ Birmingham] (UAB), University of Cambridge [UK] (CAM), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), VU University Amsterdam, Universität Leipzig [Leipzig], and Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU)

Subjects
FOS: Computer and information sciences, SQL, General Computer Science, Computer science, H.2, J.0, Big graph, 02 engineering and technology, computer.software_genre, Domain (software engineering), Computer Science - Databases, Relational database management system, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Use case, computer.programming_language, [INFO.INFO-WB]Computer Science [cs]/Web, 020207 software engineering, Databases (cs.DB), Data science, Graph, Computer Science - Distributed, Parallel, and Cluster Computing, C.3, E.0, Core (graph theory), Distributed, Parallel, and Cluster Computing (cs.DC), computer
Abstract

Graphs are, by nature, "unifying abstractions" that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed?, Communications of the ACM, 64 (9), ISSN:1557-7317, ISSN:0001-0782

Published
2021
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235. Message from the General ChairsDSC 2020

Author

M. Tamer Özsu, Hui Xiong, and Qing Li

Subjects
ComputingMilieux_THECOMPUTINGPROFESSION, Computer science, media_common.quotation_subject, ComputingMilieux_PERSONALCOMPUTING, Media studies, ComputingMilieux_LEGALASPECTSOFCOMPUTING, China, Cyberspace, GeneralLiterature_MISCELLANEOUS, Pleasure, media_common
Abstract

It is our pleasure to welcome you to the Fifth IEEE International Conference on Data Science in Cyberspace - IEEE DSC 2020 - in Hong Kong, P. R. China. IEEE DSC is a premier international conference dedicated to covering all aspects of data science in cyberspace, in both the theoretical and systems aspects.

Published
2020
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236. Regular Path Query Evaluation on Streaming Graphs

Author

M. Tamer Özsu, Anil Pacaci, Angela Bonifati, University of Waterloo [Waterloo], Base de Données (BD), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Types and Reasoning for the Web (TYREX), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Association for Computing Machinery, Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Theoretical computer science, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Semantics (computer science), Computer science, Process (computing), Databases (cs.DB), 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences, Graph, Constraint (information theory), Computer Science - Databases, 010201 computation theory & mathematics, 020204 information systems, Path (graph theory), 0202 electrical engineering, electronic engineering, information engineering, Focus (optics), Computer Science::Databases
Abstract

We study persistent query evaluation over streaming graphs, which is becoming increasingly important. We focus on navigational queries that determine if there exists a path between two entities that satisfies a user-specified constraint. We adopt the Regular Path Query (RPQ) model that specifies navigational patterns with labeled constraints. We propose deterministic algorithms to efficiently evaluate persistent RPQs under both arbitrary and simple path semantics in a uniform manner. Experimental analysis on real and synthetic streaming graphs shows that the proposed algorithms can process up to tens of thousands of edges per second and efficiently answer RPQs that are commonly used in real-world workloads., A shorter version of this paper has been accepted for publication in 2020 International Conference on Management of Data (SIGMOD 2020)

Published
2020
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237. Scalable Mining of Maximal Quasi-Cliques: An Algorithm-System Codesign Approach

Author

Jalal Khalil, Zhe Jiang, Guimu Guo, M. Tamer Özsu, and Da Yan

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Multi-core processor, Speedup, Degree (graph theory), Computer science, General Engineering, Parallel algorithm, Computer Science - Social and Information Networks, 02 engineering and technology, Load balancing (computing), Vertex (geometry), Computer Science - Distributed, Parallel, and Cluster Computing, 020204 information systems, Scalability, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), 020201 artificial intelligence & image processing, Distributed, Parallel, and Cluster Computing (cs.DC), Algorithm, MathematicsofComputing_DISCRETEMATHEMATICS
Abstract

Given a user-specified minimum degree threshold $\gamma$, a $\gamma$-quasi-clique is a subgraph $g=(V_g,E_g)$ where each vertex $v\in V_g$ connects to at least $\gamma$ fraction of the other vertices (i.e., $\lceil \gamma\cdot(|V_g|-1)\rceil$ vertices) in $g$. Quasi-clique is one of the most natural definitions for dense structures useful in finding communities in social networks and discovering significant biomolecule structures and pathways. However, mining maximal quasi-cliques is notoriously expensive. In this paper, we design parallel algorithms for mining maximal quasi-cliques on G-thinker, a recent distributed framework targeting divide-and-conquer graph mining algorithms that decomposes the mining into compute-intensive tasks to fully utilize CPU cores. However, we found that directly using G-thinker results in the straggler problem due to (i) the drastic load imbalance among different tasks and (ii) the difficulty of predicting the task running time and the time growth with task-subgraph size. We address these challenges by redesigning G-thinker's execution engine to prioritize long-running tasks for mining, and by utilizing a novel timeout strategy to effectively decompose the mining workloads of long-running tasks to improve load balancing. While this system redesign applies to many other expensive dense subgraph mining problems, this paper verifies the idea by adapting the state-of-the-art quasi-clique algorithm, Quick, to our redesigned G-thinker. We improve Quick by integrating new pruning rules, and fixing some missed boundary cases that could lead to missed results. Extensive experiments verify that our new solution scales well with the number of CPU cores, achieving 201$\times$ runtime speedup when mining a graph with 3.77M vertices and 16.5M edges in a 16-node cluster., Comment: Guimu Guo and Da Yan are parallel first authors; this is the full version of our PVLDB 2021 paper with the same title

Published
2020

238. G-thinker: A Distributed Framework for Mining Subgraphs in a Big Graph

Author

M. Tamer Özsu, Mashiur Rahman Chowdhury, Guimu Guo, Wei-Shinn Ku, John C. S. Lui, and Da Yan

Subjects
Clique, Speedup, Computer science, 020204 information systems, Computation, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, Parallel computing, Cache, Time complexity, Scheduling (computing), Vertex (geometry)
Abstract

Mining from a big graph those subgraphs that satisfy certain conditions is useful in many applications such as community detection and subgraph matching. These problems have a high time complexity, but existing systems to scale them are all IO-bound in execution. We propose the first truly CPU-bound distributed framework called G-thinker that adopts a user-friendly subgraph-centric vertex-pulling API for writing distributed subgraph mining algorithms. To utilize all CPU cores of a cluster, G-thinker features (1) a highly-concurrent vertex cache for parallel task access and (2) a lightweight task scheduling approach that ensures high task throughput. These designs well overlap communication with computation to minimize the CPU idle time. Extensive experiments demonstrate that G-thinker achieves orders of magnitude speedup compared even with the fastest existing subgraph-centric system, and it scales well to much larger and denser real network data. G-thinker is open-sourced at http://bit.ly/gthinker with detailed documentation.

Published
2020
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241. Response to 'Scale Up or Scale Out for Graph Processing'

Author

M. Tamer Özsu and Semih Salihoglu

Subjects
0301 basic medicine, Multi-core processor, Random access memory, Graph analytics, Theoretical computer science, Computer Networks and Communications, Computer science, business.industry, Computation, 020206 networking & telecommunications, 02 engineering and technology, 03 medical and health sciences, 030104 developmental biology, Scalability, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), The Internet, Graphics, business
Abstract

In this article, the authors provide their views on whether organizations should scale up or scale out their graph computations. This question was explored in a previous installment of this column by Jimmy Lin, where he made a case for scale-up through several examples. In response, the authors discuss three cases for scale-out.

Published
2018
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