Abstract
Multiple applications e.g., energy consumption meters, temperature or pressure sensors, generate series of discrete data. Such data have two characteristics, namely: they are naturally ordered by time and are frequently represented as intervals. Most of the research contributions, commercial software, or prototypes either (1) allow to analyze set oriented data, neglecting their order and duration or (2) represent intervals as discrete collection of points stored in tables. In this paper, based on our interval OLAP data model, we propose a method for representing interval data by means of functions and show that it is feasible to aggregate such data along hierarchical dimensions - in an OLAP-like style. To this end, we implemented a micro-prototype using Oracle PL/SQL. Its experimental evaluation showed that the concept is more space efficient and offers better performance than traditional approaches for some classes of analytical queries.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aster nPath. http://developer.teradata.com/aster/articles/aster-npath-guide. Accessed 15 Jan 2016
Big data processing with Apache Spark. http://www.infoq.com/articles/apache-spark-introduction. Accessed 6 Mar 2016
InfluxDB - Time Series Data Storage. https://influxdata.com/time-series-platform/influxdb/. Accessed 15 Mar 2016
Open TSDB. http://opentsdb.net/index.html. Accessed 15 Mar 2016
Bebel, B., Cichowicz, T., Morzy, T., Rytwiński, F., Wrembel, R., Koncilia, C.: Sequential data analytics by means of Seq-SQL language. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds.) DEXA 2015. LNCS, vol. 9261, pp. 416–431. Springer, Heidelberg (2015)
Bębel, B., Morzy, M., Morzy, T., Królikowski, Z., Wrembel, R.: OLAP-like analysis of time point-based sequential data. In: Castano, S., Vassiliadis, P., Lakshmanan, L.V.S., Lee, M.L. (eds.) ER 2012 Workshops 2012. LNCS, vol. 7518, pp. 153–161. Springer, Heidelberg (2012)
Chaudhuri, S., Dayal, U.: An overview of data warehousing and OLAP technology. ACM SIGMOD Rec. 26(1), 65–74 (1997)
Chawathe, S.S., Krishnamurthy, V., Ramachandran, S., Sarma, S.: Managing RFID data. In: Proceedings of International Conference on Very Large Data Bases, pp. 1189–1195 (2004)
Chui, C.K., Kao, B., Lo, E., Cheung, D.: S-OLAP: an olap system for analyzing sequence data. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 1131–1134. ACM (2010)
Chui, C.K., Lo, E., Kao, B., Ho, W.-S.: Supporting ranking pattern-based aggregate queries in sequence data cubes. In: Proceedings of ACM Conference on Information and Knowledge Management, pp. 997–1006. ACM (2009)
Eder, J., Koncilia, C., Morzy, T.: The COMET metamodel for temporal data warehouses. In: Pidduck, A.B., Mylopoulos, J., Woo, C.C., Ozsu, M.T. (eds.) CAiSE 2002. LNCS, vol. 2348, pp. 83–99. Springer, Heidelberg (2002)
Gonzalez, H., Han, J., Li, X.: FlowCube: constructing RFID flowcubes for multi-dimensional analysis of commodity flows. In: Proceedings of International Conference on Very Large Data Bases, pp. 834–845. VLDB Endowment (2006)
Gonzalez, H., Han, J., Li, X., Klabjan, D.: Warehousing and analyzing massive RFID data sets. In: Proceedings of International Conference on Data Engineering, p. 83 (2006)
Goralwalla, I.A., Tansel, A.U., Ozsu, M.T.: Experimenting with temporal relational databases. In: Proceedings of ACM Conference on Information and Knowledge Management, pp. 296–303 (1995)
Han, J., Chen, Y., Dong, G., Pei, J., Wah, B.W., Wang, J., Cai, Y.D.: Stream Cube: an architecture for multi-dimensional analysis of data streams. Distrib. Parallel Databases 18(2), 173–197 (2005)
Jensen, C.S., Lomet, D.B.: Transaction timestamping in (temporal) databases. In: Proceedings of International Conference on Very Large Data Bases, pp. 441–450 (2001)
Koncilia, C., Morzy, T., Wrembel, R., Eder, J.: Interval OLAP: analyzing interval data. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 233–244. Springer, Heidelberg (2014)
Lerner, A., Shasha, D.: Aquery: query language for ordered data, optimization techniques, and experiments. In: Proceedings of International Conference on Very Large Data Bases, pp. 345–356 (2003)
Liu, M., Rundensteiner, E., Greenfield, K., Gupta, C., Wang, S., Ari, I., Mehta, A.: E-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 889–900. ACM (2011)
Liu, M., Rundensteiner, E.A.: Event sequence processing: new models and optimization techniques. In: Proceedings of SIGMOD PhD Workshop on Innovative Database Research, pp. 7–12 (2010)
Lo, E., Kao, B., Ho, W.-S., Lee, S.D., Chui, C.K., Cheung, D.W.: OLAP on sequence data. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 649–660 (2008)
Meisen, P., Keng, D., Meisen, T., Recchioni, M., Jeschke, S.: Bitmap-based on-line analytical processing of time interval data. In: International Conference on Information Technology-New Generations, pp. 20–26 (2015)
Meisen, P., Keng, D., Meisen, T., Recchioni, M., Jeschke, S.: TIDAQL: a query language enabling on-line analytical processing of time interval data. In: Proceedings of International Conference on Enterprise Information Systems (2015)
Melton, J., (ed.): Working draft database language SQL - part 15: Row pattern recognition (SQL/RPR). ANSI INCITS DM32.2-2011-00005 (2011)
Mendelzon, A.O., Vaisman, A.A.: Temporal queries in OLAP. In: Proceedings of International Conference on Very Large Data Bases, pp. 242–253 (2000)
Mörchen, F.: Unsupervised pattern mining from symbolic temporal data. SIGKDD Explor. Newsl. 9(1), 41–55 (2007)
Perera, K.S., Hahmann, M., Lehner, W., Pedersen, T.B., Thomsen, C.: Modeling large time series for efficient approximate query processing. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M.A. (eds.) DASFAA 2015 Workshops. LNCS, vol. 9052, pp. 190–204. Springer, Heidelberg (2015)
Ramakrishnan, R., Donjerkovic, D., Ranganathan, A., Beyer, K.S., Krishnaprasad, M.: SRQL: Sorted relational query language. In: Proceedings of International Conference on Scientific and Statistical Database Management, pp. 84–95 (1998)
Sadri, R., Zaniolo, C., Zarkesh, A., Adibi, J.: Optimization of sequence queries in database systems. In: Proceedings of ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 71–81 (2001)
Sadri, R., Zaniolo, C., Zarkesh, A., Adibi, J.: Expressing and optimizing sequence queries in database systems. ACM Trans. Database Syst. 29(2), 282–318 (2004)
Seshadri, P., Livny, M., Ramakrishnan, R.: Sequence query processing. ACM SIGMOD Rec. 23(2), 430–441 (1994)
Seshadri, P., Livny, M., Ramakrishnan, R.: SEQ: a model for sequence databases. In: Proceedings of International Conference on Data Engineering, pp. 232–239 (1995)
Seshadri, P., Livny, M., Ramakrishnan, R.: The design and implementation of a sequence database system. In: Proceedings of International Conference on Very Large Data Bases, pp. 99–110 (1996)
Snodgrass, R. (ed.): The TSQL2 Temporal Query Language. Kluwer Academic Publishers, Norwell (1995)
Thiagarajan, A., Madden, S.: Querying continuous functions in a database system. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 791–804 (2008)
Witkowski, A.: Analyze this! Analytical power in SQL, more than you ever dreamt of. Oracle Open World (2012)
Zhang, Y., Kersten, M., Manegold, S.: SciQL: array data processing inside an rdbms. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 1049–1052 (2013)
Acknowledgement
The research of Gastón Bakkalian has been funded by the European Commission through the “Erasmus Mundus Joint Doctorate Information Technologies for Business Intelligence Doctoral College (IT4BI-DC)”. The research of Robert Wrembel has been funded by the Polish National Science Center, grant “Analytical processing and mining of sequential data: models, algorithms, and data structures”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Bakkalian, G., Koncilia, C., Wrembel, R. (2016). On Representing Interval Measures by Means of Functions. In: Bellatreche, L., Pastor, Ó., Almendros Jiménez, J., Aït-Ameur, Y. (eds) Model and Data Engineering. MEDI 2016. Lecture Notes in Computer Science(), vol 9893. Springer, Cham. https://doi.org/10.1007/978-3-319-45547-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-45547-1_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45546-4
Online ISBN: 978-3-319-45547-1
eBook Packages: Computer ScienceComputer Science (R0)