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Historical Graphs: Models, Storage, Processing

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Business Intelligence and Big Data (eBISS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 324))

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Abstract

Historical graphs capture the evolution of graphs through time. A historical graph can be modeled as a sequence of graph snapshots, where each snapshot corresponds to the state of the graph at the corresponding time instant. There is rich information in the history of the graph not present in just the current snapshot of the graph. In this chapter, we present logical and physical models, query types, systems and algorithms for managing historical graphs. We also highlight promising directions for future work.

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References

  1. Aggarwal, C.C., Subbian, K.: Evolutionary network analysis: a survey. ACM Comput. Surv. 47(1), 1–36 (2014)

    Article  Google Scholar 

  2. Akiba, T., Iwata, Y., Yoshida, Y.: Dynamic and historical shortest-path distance queries on large evolving networks by pruned landmark labeling. In: 23rd International World Wide Web Conference, WWW 2014, Seoul, Republic of Korea, 7–11 April 2014, pp. 237–248 (2014)

    Google Scholar 

  3. Anagnostopoulos, A., Kumar, R., Mahdian, M., Upfal, E., Vandin, F.: Algorithms on evolving graphs. In: Innovations in Theoretical Computer Science 2012, Cambridge, MA, USA, 8–10 January 2012, pp. 149–160 (2012)

    Google Scholar 

  4. Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J.L., Vrgoc, D.: Foundations of modern query languages for graph databases. ACM Comput. Surv. 50(5), 68:1–68:40 (2017)

    Article  Google Scholar 

  5. Böhlen, M.H., Busatto, R., Jensen, C.S.: Point-versus interval-based temporal data models. In: Proceedings of the Fourteenth International Conference on Data Engineering, Orlando, Florida, USA, 23–27 February 1998, pp. 192–200 (1998)

    Google Scholar 

  6. Böhlen, M.H., Dignös, A., Gamper, J., Jensen, C.S.: Temporal data management: an overview. In: Business Intelligence - 7th European Summer School, eBISS 2017, Brussels, Belgium, 2–7 July 2017. Tutorial Lectures (2017)

    Google Scholar 

  7. Cattuto, C., Quaggiotto, M., Panisson, A., Averbuch, A.: Time-varying social networks in a graph database: a Neo4j use case. In: First International Workshop on Graph Data Management Experiences and Systems, GRADES 2013, co-loated with SIGMOD/PODS 2013, New York, NY, USA, 24 June 2013, p. 11 (2013)

    Google Scholar 

  8. Cheng, R., Hong, J., Kyrola, A., Miao, Y., Weng, X., Wu, M., Yang, F., Zhou, L., Zhao, F., Chen, E.: Kineograph: taking the pulse of a fast-changing and connected world. In: European Conference on Computer Systems, Proceedings of the Seventh EuroSys Conference 2012, EuroSys 2012, Bern, Switzerland, 10–13 April 2012, pp. 85–98 (2012)

    Google Scholar 

  9. Durand, G.C., Pinnecke, M., Broneske, D., Saake, G.: Backlogs and interval timestamps: building blocks for supporting temporal queries in graph databases. In: Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017), Venice, Italy, 21–24 March 2017 (2017)

    Google Scholar 

  10. Fan, W., Hu, C., Tian, C.: Incremental graph computations: doable and undoable. In: Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD Conference 2017, Chicago, IL, USA, 14–19 May 2017, pp. 155–169 (2017)

    Google Scholar 

  11. Gelly: Documentation (2018). https://flink.apache.org/news/2015/08/24/introducing-flink-gelly.html. Accessed Jan 2018

  12. Apache Giraph: Documentation (2018). http://giraph.apache.org/literature.html. Accessed Jan 2018

  13. Gonzalez, J.E., Low, Y., Gu, H., Bickson, D., Guestrin, C.: Powergraph: distributed graph-parallel computation on natural graphs. In: 10th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2012, Hollywood, CA, USA, 8–10 October 2012, pp. 17–30 (2012)

    Google Scholar 

  14. GraphX: Programming Guide (2018). https://spark.apache.org/docs/latest/graphx-programming-guide.html. Accessed Jan 2018

  15. Han, W., Miao, Y., Li, K., Wu, M., Yang, F., Zhou, L., Prabhakaran, V., Chen, W., Chen, E.: Chronos: a graph engine for temporal graph analysis. In: Ninth Eurosys Conference 2014, EuroSys 2014, Amsterdam, The Netherlands, 13–16 April 2014, pp. 1:1–1:14 (2014)

    Google Scholar 

  16. Hayashi, T., Akiba, T., Kawarabayashi, K.: Fully dynamic shortest-path distance query acceleration on massive networks. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, 24–28 October 2016, pp. 1533–1542 (2016)

    Google Scholar 

  17. Huo, W., Tsotras, V.J.: Efficient temporal shortest path queries on evolving social graphs. In: Conference on Scientific and Statistical Database Management, SSDBM 2014, Aalborg, Denmark, 30 June–02 July 2014, pp. 38:1–38:4 (2014)

    Google Scholar 

  18. Padmanabha Iyer, A., Li, L.E., Das, T., Stoica, I.: Time-evolving graph processing at scale. In: Proceedings of the Fourth International Workshop on Graph Data Management Experiences and Systems, Redwood Shores, CA, USA, 24 June 2016, p. 5 (2016)

    Google Scholar 

  19. Jensen, C.S., Snodgrass, R.T.: Temporal data management. IEEE Trans. Knowl. Data Eng. 11(1), 36–44 (1999)

    Article  Google Scholar 

  20. Ju, X., Williams, D., Jamjoom, H., Shin, K.G.: Version traveler: fast and memory-efficient version switching in graph processing systems. In: 2016 USENIX Annual Technical Conference, USENIX ATC 2016, Denver, CO, USA, 22–24 June 2016, pp. 523–536 (2016)

    Google Scholar 

  21. Junghanns, M., Petermann, A., Neumann, M., Rahm, E.: Management and analysis of big graph data: current systems and open challenges. In: Zomaya, A., Sakr, S. (eds.) Handbook of Big Data Technologies, pp. 457–505. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49340-4_14

    Chapter  Google Scholar 

  22. Kalavri, V., Vlassov, V., Haridi, S.: High-level programming abstractions for distributed graph processing. IEEE Trans. Knowl. Data Eng. 30(2), 305–324 (2018)

    Article  Google Scholar 

  23. Kang, U., Tsourakakis, C.E., Faloutsos, C.: PEGASUS: a peta-scale graph mining system. In: ICDM 2009, The Ninth IEEE International Conference on Data Mining, Miami, Florida, USA, 6–9 December 2009, pp. 229–238 (2009)

    Google Scholar 

  24. Khurana, U., Deshpande, A.: Efficient snapshot retrieval over historical graph data. In: 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, 8–12 April 2013, pp. 997–1008 (2013)

    Google Scholar 

  25. Khurana, U., Deshpande, A.: Storing and analyzing historical graph data at scale. In: Proceedings of the 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, 15–16 March 2016, pp. 65–76 (2016)

    Google Scholar 

  26. Koloniari, G., Pitoura, E.: Partial view selection for evolving social graphs. In: First International Workshop on Graph Data Management Experiences and Systems, GRADES 2013, co-loated with SIGMOD/PODS 2013, New York, NY, USA, 24 June 2013, p. 9 (2013)

    Google Scholar 

  27. Koloniari, G., Souravlias, D., Pitoura, E.: On graph deltas for historical queries. CoRR, abs/1302.5549 (2013). Proceedings of 1st Workshop on Online Social Systems (WOSS) 2012, in conjunction with VLDB 2012

    Google Scholar 

  28. Kosmatopoulos, A., Tsichlas, K., Gounaris, A., Sioutas, S., Pitoura, E.: HiNode: an asymptotically space-optimal storage model for historical queries on graphs. Distrib. Parallel Databases 35(3–4), 249–285 (2017)

    Article  Google Scholar 

  29. Labouseur, A.G., Birnbaum, J., Olsen, P.W., Spillane, S.R., Vijayan, J., Hwang, J.-H., Han, W.-S.: The G* graph database: efficiently managing large distributed dynamic graphs. Distrib. Parallel Databases 33(4), 479–514 (2015)

    Article  Google Scholar 

  30. Low, Y., Gonzalez, J., Kyrola, A., Bickson, D., Guestrin, C., Hellerstein, J.M.: Distributed GraphLab: a framework for machine learning in the cloud. PVLDB 5(8), 716–727 (2012)

    Google Scholar 

  31. Macko, P., Marathe, V.J., Margo, D.W., Seltzer, M.I.: LLAMA: efficient graph analytics using large multiversioned arrays. In: 31st IEEE International Conference on Data Engineering, ICDE 2015, Seoul, South Korea, 13–17 April 2015, pp. 363–374 (2015)

    Google Scholar 

  32. Malewicz, G., Austern, M.H., Bik, A.J.C., Dehnert, J.C., Horn, I., Leiser, N., Czajkowski, G.: Pregel: a system for large-scale graph processing. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, 6–10 June 2010, pp. 135–146 (2010)

    Google Scholar 

  33. McGregor, A.: Graph stream algorithms: a survey. SIGMOD Rec. 43(1), 9–20 (2014)

    Article  MathSciNet  Google Scholar 

  34. Miao, Y., Han, W., Li, K., Wu, M., Yang, F., Zhou, L., Prabhakaran, V., Chen, E., Chen, W.: ImmortalGraph: a system for storage and analysis of temporal graphs. TOS 11(3), 14:1–14:34 (2015)

    Article  Google Scholar 

  35. Moffitt, V.Z., Stoyanovich, J.: Towards a distributed infrastructure for evolving graph analytics. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, 11–15 April 2016, Companion Volume, pp. 843–848 (2016)

    Google Scholar 

  36. Moffitt, V.Z., Stoyanovich, J.: Temporal graph algebra. In: Proceedings of the 16th International Symposium on Database Programming Languages, DBPL 2017, Munich, Germany, 1 September 2017, pp. 10:1–10:12 (2017)

    Google Scholar 

  37. Moffitt, V.Z., Stoyanovich, J.: Towards sequenced semantics for evolving graphs. In: Proceedings of the 20th International Conference on Extending Database Technology, EDBT 2017, Venice, Italy, 21–24 March 2017, pp. 446–449 (2017)

    Google Scholar 

  38. Ren, C., Lo, E., Kao, B., Zhu, X., Cheng, R.: On querying historical evolving graph sequences. PVLDB 4(11), 726–737 (2011)

    Google Scholar 

  39. Ren, C., Lo, E., Kao, B., Zhu, X., Cheng, R., Cheung, D.W.: Efficient processing of shortest path queries in evolving graph sequences. Inf. Syst. 70, 18–31 (2017)

    Article  Google Scholar 

  40. Salzberg, B., Tsotras, V.J.: Comparison of access methods for time-evolving data. ACM Comput. Surv. 31(2), 158–221 (1999)

    Article  Google Scholar 

  41. Semertzidis, K., Pitoura, E.: Durable graph pattern queries on historical graphs. In: 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, 16–20 May 2016, pp. 541–552 (2016)

    Google Scholar 

  42. Semertzidis, K., Pitoura, E.: Time traveling in graphs using a graph database. In: Proceedings of the Workshops of the EDBT/ICDT 2016 Joint Conference, EDBT/ICDT Workshops 2016, Bordeaux, France, 15 March 2016 (2016)

    Google Scholar 

  43. Semertzidis, K., Pitoura, E.: Historical traversals in native graph databases. In: Kirikova, M., Nørvåg, K., Papadopoulos, G.A. (eds.) ADBIS 2017. LNCS, vol. 10509, pp. 167–181. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66917-5_12

    Chapter  Google Scholar 

  44. Semertzidis, K., Pitoura, E.: Top-k durable graph pattern queries on temporal graphs. IEEE Trans. Knowl. Data Eng. (2018, to appear)

    Google Scholar 

  45. Semertzidis, K., Pitoura, E., Lillis, K.: TimeReach: historical reachability queries on evolving graphs. In: Proceedings of the 18th International Conference on Extending Database Technology, EDBT 2015, Brussels, Belgium, 23–27 March 2015, pp. 121–132 (2015)

    Google Scholar 

  46. The Neo4j Team: Manual (2018). https://neo4j.com/docs/developer-manual/3.3/. Accessed Jan 2018

  47. Then, M., Kersten, T., Günnemann, S., Kemper, A., Neumann, T.: Automatic algorithm transformation for efficient multi-snapshot analytics on temporal graphs. PVLDB 10(8), 877–888 (2017)

    Google Scholar 

  48. Apache TinkerPop (2018). http://tinkerpop.apache.org/. Accessed Jan 2018

  49. Huanhuan, W., Cheng, J., Huang, S., Ke, Y., Yi, L., Yanyan, X.: Path problems in temporal graphs. PVLDB 7(9), 721–732 (2014)

    Google Scholar 

  50. Xie, W., Tian, Y., Sismanis, Y., Balmin, A., Haas, P.J.: Dynamic interaction graphs with probabilistic edge decay. In: 31st IEEE International Conference on Data Engineering, ICDE 2015, Seoul, South Korea, 13–17 April 2015, pp. 1143–1154 (2015)

    Google Scholar 

  51. Yan, D., Bu, Y., Tian, Y., Deshpande, A.: Big graph analytics platforms. Found. Trends Databases 7(1–2), 1–195 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering Department, University of Ioannina, Ioannina, Greece

    Evaggelia Pitoura

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  1. Evaggelia Pitoura
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Correspondence to Evaggelia Pitoura .

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  1. Université Libre de Bruxelles, Brussels, Belgium

    Esteban Zimányi

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Pitoura, E. (2018). Historical Graphs: Models, Storage, Processing. In: Zimányi, E. (eds) Business Intelligence and Big Data. eBISS 2017. Lecture Notes in Business Information Processing, vol 324. Springer, Cham. https://doi.org/10.1007/978-3-319-96655-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-96655-7_4

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