1. bookVolume 29 (2019): Issue 1 (March 2019)
    Exploring Complex and Big Data (special section, pp. 7-91), Johann Gamper, Robert Wrembel (Eds.)
Journal Details
License
Format
Journal
eISSN
2083-8492
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English
Open Access

Modeling and querying facts with period timestamps in data warehouses

Published Online: 29 Mar 2019
Volume & Issue: Volume 29 (2019) - Issue 1 (March 2019) - Exploring Complex and Big Data (special section, pp. 7-91), Johann Gamper, Robert Wrembel (Eds.)
Page range: 31 - 49
Received: 18 Apr 2018
Accepted: 27 Nov 2018
Journal Details
License
Format
Journal
eISSN
2083-8492
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English

Ahmed, W., Zimányi, E. and Wrembel, R. (2014). A logical model for multiversion data warehouses, Proceedings of the 16th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2014, Munich, Germany, pp. 23–34.Search in Google Scholar

Bebel, B., Cichowicz, T., Morzy, T., Rytwinski, F., Wrembel, R. and Koncilia, C. (2015). Sequential data analytics by means of Seq-SQL language, Proceedings of the 26th International Conference on Database and Expert Systems Applications, DEXA 2015, Valencia, Spain, Part I, pp. 416–431.Search in Google Scholar

Ben-Gan, I., Machanic, A., Sarka, D. and Farlee, K. (2015). TSQL Querying, Microsoft Press, Redmond, WA.Search in Google Scholar

Blaschka, M., Sapia, C. and Höfling, G. (1999). On schema evolution in multidimensional databases, Proceedings of the 1st International Conference on Data Warehousing and Knowledge Discovery, DaWaK 1999, Florence, Italy, pp. 153–164.Search in Google Scholar

Bliujute, R., Saltenis, S., Slivinskas, G. and Jensen, C.S. (1998). Systematic change management in dimensional data warehousing, Proceedings of the 3rd International Baltic Workshop on DB and IS, Riga, Latvia, pp. 27–41.Search in Google Scholar

Böhlen, M.H., Dignös, A., Gamper, J. and Jensen, C.S. (2018). Temporal data management—an overview, in E. Zimányi (Ed.), Business Intelligence and Big Data, Springer International Publishing, Cham, pp. 51–83.10.1007/978-3-319-96655-7_3Search in Google Scholar

Böhlen, M.H., Gamper, J. and Jensen, C.S. (2006a). An algebraic framework for temporal attribute characteristics, Annals of Mathematics and Artificial Intelligence46(3): 349–374.10.1007/s10472-006-9022-5Search in Google Scholar

Böhlen, M.H., Gamper, J. and Jensen, C.S. (2006b). Multi-dimensional aggregation for temporal data, Proceedings of the 10th International Conference on Extending Database Technology, EDBT 2006, Munich, Germany, pp. 257–275.10.1007/11687238_18Search in Google Scholar

Böhlen, M.H., Gamper, J., Jensen, C.S. and Snodgrass, R.T. (2009). SQL-based temporal query languages, in L. Liu and M. Tamer Özsu (Eds.), Encyclopedia of Database Systems, Springer, New York, NY, pp. 2762–2768.10.1007/978-0-387-39940-9_1525Search in Google Scholar

Bouros, P. and Mamoulis, N. (2017). A forward scan based plane sweep algorithm for parallel interval joins, Proceedings of the VLDB Endowment10(11): 1346–1357.10.14778/3137628.3137644Search in Google Scholar

Cafagna, F. and Böhlen, M.H. (2017). Disjoint interval partitioning, The VLDB Journal26(3): 447–466.10.1007/s00778-017-0456-7Search in Google Scholar

Dignös, A., Böhlen, M.H. and Gamper, J. (2012). Temporal alignment, Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2012, Scottsdale, AZ, USA, pp. 433–444.Search in Google Scholar

Dignös, A., Böhlen, M.H. and Gamper, J. (2013). Query time scaling of attribute values in interval timestamped databases, Proceedings of the 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, pp. 1304–1307.Search in Google Scholar

Dignös, A., Böhlen, M.H., Gamper, J. and Jensen, C.S. (2016). Extending the kernel of a relational DBMS with comprehensive support for sequenced temporal queries, ACM Transactions on Database Systems41(4): 26:1–26:46.10.1145/2967608Search in Google Scholar

Eder, J., Koncilia, C. and Morzy, T. (2002). The COMET metamodel for temporal data warehouses, Proceedings of the 14th International Conference on Advanced Information Systems Engineering, CAiSE 2002, Toronto, Canada, pp. 83–99.Search in Google Scholar

Faisal, S. and Sarwar, M. (2014). Handling slowly changing dimensions in data warehouses, Journal of Systems and Software94: 151–160.10.1016/j.jss.2014.03.072Search in Google Scholar

Gao, D., Jensen, C.S., Snodgrass, R.T. and Soo, M.D. (2005). Join operations in temporal databases, The VLDB Journal14(1): 2–29.10.1007/s00778-003-0111-3Search in Google Scholar

Garani, G., Adam, G.K. and Ventzas, D. (2016). Temporal data warehouse logical modelling, International Journal of Data Mining, Modelling and Management8(2): 144–159.10.1504/IJDMMM.2016.077156Search in Google Scholar

Golfarelli, M. and Rizzi, S. (2009a). Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill, Inc., New York, NY.Search in Google Scholar

Golfarelli, M. and Rizzi, S. (2009b). A survey on temporal data warehousing, International Journal of Data Warehousing and Mining5(1): 1–17.10.4018/jdwm.2009010101Search in Google Scholar

Golfarelli, M. and Rizzi, S. (2011). Temporal data warehousing: Approaches and techniques, in D. Taniar and L. Chen (Eds.), Integrations of Data Warehousing, Data Mining and Database Technologies—Innovative Approaches, Information Science Reference, London, pp. 1–18.10.4018/978-1-60960-537-7.ch001Search in Google Scholar

Goller, M. and Berger, S. (2013). Slowly changing measures, Proceedings of the 16th International Workshop on Data Warehousing and OLAP, DOLAP 2013, San Francisco, CA, USA, pp. 47–54.Search in Google Scholar

Goller, M. and Berger, S. (2015). Handling measurement function changes with slowly changing measures, Information Systems53: 107–123.10.1016/j.is.2014.12.009Search in Google Scholar

Höpken, W., Fuchs, M., Höll, G., Keil, D. and Lexhagen, M. (2013). Multi-dimensional data modelling for a tourism destination data warehouse, Proceedings of the International Conference on Information and Communication Technologies in Tourism 2013, Insbrusck, Austria, pp. 157–169.Search in Google Scholar

Jensen, C.S., Pedersen, T.B. and Thomsen, C. (2010). Multidimensional Databases and Data Warehousing, Synthesis Lectures on Data Management, Morgan & Claypool Publishers, San Rafael, CA.10.2200/S00299ED1V01Y201009DTM009Search in Google Scholar

Jensen, C.S. and Snodgrass, R.T. (2009). Temporal database, in L. Liu and M. Tamer Özsu (Eds.), Encyclopedia of Database Systems, Springer, New York, NY, p. 2957.10.1007/978-0-387-39940-9_395Search in Google Scholar

Jensen, C.S., Soo, M.D. and Snodgrass, R.T. (1994). Unifying temporal data models via a conceptual model, Information Systems19(7): 513–547.10.1016/0306-4379(94)90013-2Search in Google Scholar

Kimball, R. and Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edn., Wiley Publishing, Hoboken, NJ.Search in Google Scholar

Kline, N. and Snodgrass, R.T. (1995). Computing temporal aggregates, Proceedings of the 11th International Conference on Data Engineering, ICDE 1995, Taipei, Taiwan, pp. 222–231.Search in Google Scholar

Koncilia, C. (2003). A bi-temporal data warehouse model, Proceedings of the 15th Conference on Advanced Information Systems Engineering, CAiSE 2003, Klagenfurt, Austria, Vol. 74.Search in Google Scholar

Koncilia, C., Morzy, T., Wrembel, R. and Eder, J. (2014). Interval OLAP: Analyzing interval data, Proceedings of the 16th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2014, Munich, Germany, pp. 233–244.Search in Google Scholar

Lenz, H. and Shoshani, A. (1997). Summarizability in OLAP and statistical data bases, Proceedings of the 9th International Conference on Scientific and Statistical Database Management, SSDBM 1997, Olympia, WA, USA, pp. 132–143.Search in Google Scholar

Lorentzos, N.A. (2009). Period-stamped temporal models, in L. Liu and M. Tamer Özsu (Eds.), Encyclopedia of Database Systems, Springer, New York, NY, pp. 2094–2098.10.1007/978-0-387-39940-9_266Search in Google Scholar

Malinowski, E. and Zimányi, E. (2008). A conceptual model for temporal data warehouses and its transformation to the ER and the object-relational models, Data & Knowledge Engineering64(1): 101–133.10.1016/j.datak.2007.06.020Search in Google Scholar

Melton, J. and Simon, A.R. (2002). Advanced SQL query expressions, in J. Melton and A.R. Simon (Eds.), SQL: 1999, Morgan Kaufmann, Burlington, VA, pp. 265–353.10.1016/B978-155860456-8/50010-2Search in Google Scholar

Moon, B., Vega Lopez, I.F. and Immanuel, V. (2003). Efficient algorithms for large-scale temporal aggregation, IEEE Transactions on Knowledge and Data Engineering15(3): 744–759.10.1109/TKDE.2003.1198403Search in Google Scholar

Piatov, D. and Helmer, S. (2017). Sweeping-based temporal aggregation, Proceedings of the 15th International Symposium on Advances in Spatial and Temporal Databases, SSTD 2017, Arlington, VA, USA, pp. 125–144.Search in Google Scholar

Piatov, D., Helmer, S. and Dignös, A. (2016). An interval join optimized for modern hardware, Proceedings of the 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, pp. 1098–1109.Search in Google Scholar

Toman, D. (2009). Point-stamped temporal models, in L. Liu and M. Tamer Özsu (Eds.), Encyclopedia of Database Systems, Springer, New York, NY, pp. 2119–2123.10.1007/978-0-387-39940-9_269Search in Google Scholar

Wrembel, R. and Bebel, B. (2007). Metadata management in a multiversion data warehouse, Journal on Data Semantics8: 118–157.10.1007/978-3-540-70664-9_5Search in Google Scholar

Yang, J. and Widom, J. (2003). Incremental computation and maintenance of temporal aggregates, The VLDB Journal12(3): 262–283.10.1007/s00778-003-0107-zSearch in Google Scholar

Zhang, D., Markowetz, A., Tsotras, V.J., Gunopulos, D. and Seeger, B. (2001). Efficient computation of temporal aggregates with range predicates, Proceedings of the 20th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS 2001, Santa Barbara, CA, USA, pp. 237–245.Search in Google Scholar

Zhang, D., Tsotras, V.J. and Seeger, B. (2002). Efficient temporal join processing using indices, Proceedings of the 18th International Conference on Data Engineering, ICDE 2002, San Jose, CA, USA, pp. 103–113.Search in Google Scholar

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