TY - JOUR AU - Hinkel, Georg AU - Garcia-Dominguez, Antonio AU - Schöne, René AU - Boronat, Artur AU - Tisi, Massimo AU - Le Calvar, Théo AU - Jouault, Frederic AU - Marton, József AU - Nyíri, Tamás AU - Antal, János Benjamin AU - Elekes, Márton AU - Szárnyas, Gábor TI - A cross-technology benchmark for incremental graph queries JF - SOFTWARE AND SYSTEMS MODELING J2 - SOFTW SYST MODEL VL - 21 PY - 2022 IS - 2 SP - 755 EP - 804 PG - 50 SN - 1619-1366 DO - 10.1007/s10270-021-00927-5 UR - https://m2.mtmt.hu/api/publication/32538562 ID - 32538562 N1 - Wiesbaden, Germany Aston University, Birmingham, United Kingdom Technische Universität Dresden, Software Technology Group, Dresden, Germany School of Informatics, University of Leicester, Leicester, United Kingdom IMT Atlantique, LS2N (UMR CNRS 6004), Nantes, France DIRO, Université de Montréal, Montreal, Canada ERIS, ESEO-TECH, Angers, France Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary CWI, Amsterdam, Netherlands Export Date: 29 April 2022 Correspondence Address: Elekes, M.; Department of Measurement and Information Systems, Hungary; email: elekes@mit.bme.hu AB - To cope with the increased complexity of systems, models are used to capture what is considered the essence of a system. Such models are typically represented as a graph, which is queried to gain insight into the modelled system. Often, the results of these queries need to be adjusted according to updated requirements and are therefore a subject of maintenance activities. It is thus necessary to support writing model queries with adequate languages. However, in order to stay meaningful, the analysis results need to be refreshed as soon as the underlying models change. Therefore, a good execution speed is mandatory in order to cope with frequent model changes. In this paper, we propose a benchmark to assess model query technologies in the presence of model change sequences in the domain of social media. We present solutions to this benchmark in a variety of 11 different tools and compare them with respect to explicitness of incrementalization, asymptotic complexity and performance. LA - English DB - MTMT ER - TY - CHAP AU - Waudby, Jack AU - Steer, Benjamin A. AU - Karimov, Karim AU - Marton, József AU - Boncz, Peter AU - Szárnyas, Gábor ED - Nambiar, R ED - Poess, M TI - Towards Testing ACID Compliance in the LDBC Social Network Benchmark T2 - Performance Evaluation and Benchmarking PB - Springer Netherlands CY - Cham SN - 9783030849245 T3 - Lecture Notes in Computer Science, ISSN 0302-9743 ; 12752. PY - 2021 SP - 1 EP - 17 PG - 17 DO - 10.1007/978-3-030-84924-5_1 UR - https://m2.mtmt.hu/api/publication/32398430 ID - 32398430 AB - Verifying ACID compliance is an essential part of database benchmarking, because the integrity of performance results can be undermined as the performance benefits of operating with weaker safety guarantees (at the potential cost of correctness) are well known. Traditionally, benchmarks have specified a number of tests to validate ACID compliance. However, these tests have been formulated in the context of relational database systems and SQL, whereas our scope of benchmarking are systems for graph data, many of which are non-relational. This paper presents a set of data model-agnostic ACID compliance tests for the LDBC (Linked Data Benchmark Council) Social Network Benchmark suite's Interactive (SNB-I) workload, a transaction processing benchmark for graph databases. We test all ACID properties with a particular emphasis on isolation, covering 10 transaction anomalies in total. We present results from implementing the test suite on 5 database systems. LA - English DB - MTMT ER - TY - JOUR AU - Sakr, Sherif AU - Bonifati, Angela AU - Voigt, Hannes AU - Iosup, Alexandru AU - Ammar, Khaled AU - Angles, Renzo AU - Aref, Walid AU - Arenas, Marcelo AU - Besta, Maciej AU - Boncz, Peter A. AU - Daudjee, Khuzaima AU - Valle, Emanuele Della AU - Dumbrava, Stefania AU - Hartig, Olaf AU - Haslhofer, Bernhard AU - Hegeman, Tim AU - Hidders, Jan AU - Hose, Katja AU - Iamnitchi, Adriana AU - Kalavri, Vasiliki AU - Kapp, Hugo AU - Martens, Wim AU - Özsu, M. Tamer AU - Peukert, Eric AU - Plantikow, Stefan AU - Ragab, Mohamed AU - Ripeanu, Matei R. AU - Salihoglu, Semih AU - Schulz, Christian AU - Selmer, Petra AU - Sequeda, Juan F. AU - Shinavier, Joshua AU - Szárnyas, Gábor AU - Tommasini, Riccardo AU - Tumeo, Antonino AU - Uta, Alexandru AU - Varbanescu, Ana Lucia AU - Wu, Hsiang-Yun AU - Yakovets, Nikolay AU - Yan, Da AU - Yoneki, Eiko TI - The future is big graphs: a community view on graph processing systems JF - COMMUNICATIONS OF THE ACM J2 - COMMUN ACM VL - 64 PY - 2021 IS - 9 SP - 62 EP - 71 PG - 10 SN - 0001-0782 DO - 10.1145/3434642 UR - https://m2.mtmt.hu/api/publication/32241157 ID - 32241157 N1 - Cited By :7 Export Date: 14 June 2022 CODEN: CACMA Correspondence Address: Bonifati, A.; Lyon 1 University and Liris Cnrs in VilleurbanneFrance; email: angela.bonifati@univ-lyon1.fr LA - English DB - MTMT ER - TY - JOUR AU - Semeráth, Oszkár AU - Aren, A. Babikian AU - Boqi, Chen AU - Chuning, Li AU - Marussy, Kristóf AU - Szárnyas, Gábor AU - Varró, Dániel TI - Automated Generation of Consistent, Diverse and Structurally Realistic Graph Models JF - SOFTWARE AND SYSTEMS MODELING J2 - SOFTW SYST MODEL VL - 20 PY - 2021 IS - 5 SP - 1713 EP - 1734 PG - 22 SN - 1619-1366 DO - 10.1007/s10270-021-00884-z UR - https://m2.mtmt.hu/api/publication/32028274 ID - 32028274 N1 - MTA-BME Lendület Cyber-Physical Systems Res. Grp. Department of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok krt. 2, Budapest, 1117, Hungary Department of Electrical and Computer Engineering, McGill University, 3480 Rue University, Montréal, QC H3A 0E9, Canada Cited By :1 Export Date: 9 June 2022 Correspondence Address: Semeráth, O.; MTA-BME Lendület Cyber-Physical Systems Res. Grp. Department of Measurement and Information Systems, Magyar tudósok krt. 2, Hungary; email: semerath@mit.bme.hu Funding details: Natural Sciences and Engineering Research Council of Canada, NSERC, PGSD3-546810-2020, RGPIN-04573-16 Funding details: Fonds de recherche du Québec – Nature et technologies, FRQNT, 272709 Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA Funding details: Innovációs és Technológiai Minisztérium Funding text 1: We would like to thank all three reviewers for their detailed and insightful feedback. This paper was partially supported by the NSERC RGPIN-04573-16 project, the NSERC PGSD3-546810-2020 scholarship, the McGill Grad Excellence Award-90025, the Fonds de recherche du Québec - Nature et technologies (FRQNT) B1X scholarship (file number: 272709), the ÚNKP-20-4 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund, and by the NRDI Fund based on the charter of bolster issued by the NRDI Office under the auspices of the Ministry for Innovation and Technology. We would like to thank the Department of Electrical and Computer Engineering, and the School of Computer Science of McGill University for providing resources to run our measurements. During the development of the achievements, we took into consideration the goals set by the Balatonfüred System Science Innovation Cluster and the plans of the “BME Balatonfüred Knowledge Center,” supported by EFOP 4.2.1-16-2017-00021. Funding text 2: We would like to thank all three reviewers for their detailed and insightful feedback. This paper was partially supported by the NSERC RGPIN-04573-16 project, the NSERC PGSD3-546810-2020 scholarship, the McGill Grad Excellence Award-90025, the Fonds de recherche du Qu?bec - Nature et technologies (FRQNT) B1X scholarship (file number: 272709), the ?NKP-20-4 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund, and by the NRDI Fund based on the charter of bolster issued by the NRDI Office under the auspices of the Ministry for Innovation and Technology. We would like to thank the Department of Electrical and Computer Engineering, and the School of Computer Science of McGill University for providing resources to run our measurements. During the development of the achievements, we took into consideration the goals set by the Balatonf?red System Science Innovation Cluster and the plans of the ?BME Balatonf?red Knowledge Center,? supported by EFOP 4.2.1-16-2017-00021. AB - In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios. LA - English DB - MTMT ER - TY - CHAP AU - Azad, Ariful AU - Aznaveh, Mohsen Mahmoudi AU - Beamer, Scott AU - Blanco, Mark AU - Chen, Jinhao AU - D'Alessandro, Luke AU - Dathathri, Roshan AU - Davis, Tim AU - Deweese, Kevin AU - Firoz, Jesun AU - Gabb, Henry A AU - Gill, Gurbinder AU - Hegyi, Balint AU - Kolodziej, Scott AU - Low, Tze Meng AU - Lumsdaine, Andrew AU - Manlaibaatar, Tugsbayasgalan AU - Mattson, Timothy G AU - McMillan, Scott AU - Peri, Ramesh AU - Pingali, Keshav AU - Sridhar, Upasana AU - Szárnyas, Gábor AU - Zhang, Yunming AU - Zhang, Yongzhe TI - Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite T2 - 2020 IEEE International Symposium on Workload Characterization (IISWC) SN - 9781728176451 PY - 2020 SP - 216 EP - 227 PG - 12 DO - 10.1109/IISWC50251.2020.00029 UR - https://m2.mtmt.hu/api/publication/32868652 ID - 32868652 N1 - Indiana University, United States Texas Am University, United States University of California, Santa Cruz, United States Carnegie Mellon University, United States University of Texas at Austin, United States University of Washington, United States Pacific Northwest National Laboratory Intel Corporation Budapest University of Technology and Economics, Hungary Massachusetts Institute of Technology, United States Graduate University for Advanced Studies, Sokendai, Japan Cited By :6 Export Date: 9 June 2022 LA - English DB - MTMT ER - TY - CHAP AU - Elekes, Márton AU - Nagy, Attila AU - Sándor, Dávid AU - Antal, János Benjamin AU - Davis, Timothy A. AU - Szárnyas, Gábor ED - IEEE, null TI - A GraphBLAS solution to the SIGMOD 2014 Programming Contest using multi-source BFS T2 - 2020 IEEE High Performance Extreme Computing Conference (HPEC) PB - IEEE CY - Piscataway (NJ) SN - 9781728192192 PY - 2020 SP - 1 EP - 7 PG - 7 DO - 10.1109/HPEC43674.2020.9286186 UR - https://m2.mtmt.hu/api/publication/31861479 ID - 31861479 LA - English DB - MTMT ER - TY - CHAP AU - Waudby, Jack AU - Steer, Benjamin A. AU - Prat-Pérez, Arnau AU - Szárnyas, Gábor ED - Akhil, Arora ED - Semih, Salihoglu ED - Nikolay, Yakovets TI - Supporting Dynamic Graphs and Temporal Entity Deletions in the LDBC Social Network Benchmark's Data Generator T2 - Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) PB - Association for Computing Machinery (ACM) CY - New York, New York SN - 9781450380218 PY - 2020 SP - 1 EP - 8 PG - 8 DO - 10.1145/3398682.3399165 UR - https://m2.mtmt.hu/api/publication/31344297 ID - 31344297 N1 - Alibaba; et al.; IBM; Neo4j; SIGMOD; TigerGraph Conference code: 160983 Cited By :8 Export Date: 7 February 2024 Funding details: FK-128981 Funding details: Alan Turing Institute, EP/T001569/1 Funding details: Engineering and Physical Sciences Research Council, EPSRC, EP/L015358/1 Funding details: Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFIH Funding text 1: the Development and Innovation Office of Hungary (NKFIH), National Research, grant number FK-128981 Funding text 2: The authors would like to thank Peter Boncz for his suggestions. J. Waudby was supported by the Engineering and Physical Sciences Research Council, Centre for Doctoral Training in Cloud Computing for Big Data [grant number EP/L015358/1]. B. Steer was supported by the Engineering and Physical Sciences Research Council and Alan Turing Institute [grant number EP/T001569/1]. G. Szárnyas was partially supported by the MTA-BME Lendület Cyber-Physical Systems Research Group. The parameterization of the data generator was determined with input from the the Development and Innovation Office of Hungary (NKFIH), National Research, grant number FK-128981. LA - English DB - MTMT ER - TY - CHAP AU - Elekes, Márton AU - Szárnyas, Gábor ED - Anu, G. Bourgeois ED - Ramachandran, Vaidyanathan TI - An incremental GraphBLAS solution for the 2018 TTC Social Media case study T2 - 2020 IEEE 34th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020 PB - IEEE CY - Piscataway (NJ) SN - 9781728174457 PY - 2020 SP - 203 EP - 206 PG - 4 DO - 10.1109/IPDPSW50202.2020.00045 UR - https://m2.mtmt.hu/api/publication/31340716 ID - 31340716 N1 - Cited By :1 Export Date: 16 June 2022 AB - Graphs are increasingly important for modelling and analysing connected data sets. Traditionally, graph analytical tools targeted global fixed-point computations, while graph databases focused on simpler transactional read operations such as retrieving the neighbours of a node. However, recent applications of graph processing (such as financial fraud detection and serving personalized recommendations) often necessitate a mix of the two workload profiles. A potential approach to tackle these complex workloads is to formulate graph algorithms in the language of linear algebra. To this end, the recent GraphBLAS standard defines a linear algebraic graph computational model and an API for implementing such algorithms. To investigate its usability and efficiency, we have implemented a GraphBLAS solution for the "Social Media" case study of the 2018 Transformation Tool Contest. This paper presents our solution along with an incrementalized variant to improve its runtime for repeated evaluations. Preliminary results show that the GraphBLAS-based solution is competitive but implementing it requires significant development efforts. LA - English DB - MTMT ER - TY - CHAP AU - Elekes, Márton AU - Szárnyas, Gábor ED - Renczes, Balázs TI - Incremental view maintenance in graph databases: A case study in Neo4j T2 - Proceedings of the 27th PhD Mini-Symposium PB - Budapest University of Technology and Economics, Department of Measurement and Information Systems CY - Budapest SN - 9789634218074 PY - 2020 SP - 33 EP - 36 PG - 4 UR - https://m2.mtmt.hu/api/publication/31340549 ID - 31340549 AB - With the increasing amount of densely connected data sets, graph analysis has become an integral part of data processing pipelines. Therefore, the last decade saw the emergence of numerous dedicated graph analytical systems along with specialized graph database management systems. Traditionally, graph analytical tools targeted global fixed-point computations, while graph databases focused on simpler transactional read operations such as retrieving the neighbours of a node. However, recent applications of graph processing (such as financial fraud detection and serving personalized recommendations) often necessitate a mix of the two workload profiles. Following this trend, the 2018 Transformation Tool Contest (an annual competition for graph transformation tools) presented a case study that requires participants to compute complex graph queries defined on a continuously changing social network graph. The solutions are assessed based on their scalability and query reevaluation time, therefore, solutions are encouraged to incrementalize their implementations. This paper demonstrates a solution in the popular Neo4j graph database using several incrementalization techniques and compares them against the reference implementation of the case study. LA - English DB - MTMT ER - TY - CHAP AU - Aznaveh, Mohsen AU - Chen, Jinhao AU - Davis, Timothy A. AU - Hegyi, Bálint AU - Kolodziej, Scott P. AU - Mattson, Timothy G. AU - Szárnyas, Gábor ED - Li, Xiaoye Sherri ED - Bücker, H. Martin TI - Parallel GraphBLAS with OpenMP T2 - 2020 Proceedings of the SIAM Workshop on Combinatorial Scientific Computing PB - Society for Industrial and Applied Mathematics (SIAM) SN - 9781611976229 PY - 2020 SP - 138 EP - 148 PG - 11 DO - 10.1137/1.9781611976229.14 UR - https://m2.mtmt.hu/api/publication/31329295 ID - 31329295 LA - English DB - MTMT ER -