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Online view maintenance for continuous query evaluation

Soheila Dehghanzadeh, Daniele Dell'Aglio, Shen Gao, Emanuele Della Valle, Alessandra Mileo, Abraham Bernstein
The introduction of local views on the background data speeds up the query answering process, but requires maintenance processes to keep the replicated data up-to-date.  ...  The introduction of local views on the background data speeds up the query answering process, but requires maintenance processes to keep the replicated data up-todate.  ...  An efficient maintenance policy has to be introduced to keep the local view consistent with the remote data.  ... 
doi:10.5167/uzh-109964 fatcat:mtccoqo6z5anbju45tshpqs33u

KTV-Tree: Interactive Top-K Aggregation on Dynamic Large Dataset in the Cloud

Yuzhe Tang, Ling Liu, Junichi Tatemura, Hakan Hacigumus
2015 2015 IEEE 35th International Conference on Distributed Computing Systems Workshops  
Experiments are performed for evaluating the effectiveness of our solutions, in terms of query accuracy, costs and maintenance overhead.  ...  A segment treebased structure is adopted to organize the views in a hierarchical manner. A suite of protocols are proposed for incrementally maintaining the views.  ...  This incurs extra high overhead for view maintenance, which is impractical particularly for a streaming data source where data is generated continuously.  ... 
doi:10.1109/icdcsw.2015.32 dblp:conf/icdcsw/TangLTH15 fatcat:4selowu3grezle6a3salcedogy


Kai Zeng, Sameer Agarwal, Ankur Dave, Michael Armbrust, Ion Stoica
2015 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD '15  
In this demonstration, we present G-OLA, a novel mini-batch execution model that generalizes OLA to support general OLAP queries with arbitrarily nested aggregates using e cient delta maintenance techniques  ...  We have implemented G-OLA in FluoDB, a parallel online query execution framework that is built on top of the Spark cluster computing framework that can scale to massive data sets.  ...  Similar intuition is shared by online aggregation [ ] and the work on incremental view maintenance [ , , ] .  ... 
doi:10.1145/2723372.2735381 dblp:conf/sigmod/ZengADAS15 fatcat:a66fy635njbhzopnu6umkodlwu

Markov Chain for the Recommendation of Materialized Views in Real-Time Data Warehouse

Abdelaziz Abdellatif, Ali Ben Ammar, Chadlia Mazlout
2014 International Journal of Computer Science Engineering and Applications  
The aim is to reduce the complexity and the execution cost of the online selection of materialized views.  ...  Our experiment results have shown that our solution is very efficient to specify the more profitable views and to improve the query response time.  ...  Also, we are not aware of any work which continually cluster and filter views for the online selection.  ... 
doi:10.5121/ijcsea.2014.4602 fatcat:mylauem3vzbibp4ej7ma6kv6n4

Multi-scale Windowing over Trajectory Streams [chapter]

Kostas Patroumpas
2012 Lecture Notes in Computer Science  
efficient shared evaluation  Useful in expressing spatiotemporal continuous queries • Directions for further study -Incremental maintenance of multiple window states • Strategies trading off performance  ...  historical trajectories • FUTURE  predict routes / estimate trends SeCoGIS'12 Windowing over Data Streams • Continuous queries evaluated against stream "chunks" -Infeasible to retain the entire stream  ... 
doi:10.1007/978-3-642-33999-8_35 fatcat:4sncwdwk4bhmbofvwz4mebf3ka

Continuous queries over data streams

Shivnath Babu, Jennifer Widom
2001 SIGMOD record  
In this paper we focus primarily on the problem of query processing, specifically on how to define and evaluate continuous queries over data streams.  ...  The architecture also captures most previous work on continuous queries and data streams, as well as related concepts such as triggers and materialized views.  ...  Acknowledgements We are grateful to Jose Blakeley for excellent comments on an initial draft, and to the entire STREAM group at Stanford for many inspiring discussions.  ... 
doi:10.1145/603867.603884 fatcat:6ix4y2aym5dppjzpuhg5kxj4bq

How to Win a Hot Dog Eating Contest

Milos Nikolic, Mohammad Dashti, Christoph Koch
2016 Proceedings of the 2016 International Conference on Management of Data - SIGMOD '16  
In the quest for valuable information, modern big data applications continuously monitor streams of data.  ...  Batch updates are essential for enabling distributed incremental view maintenance and amortizing the cost of network communication and synchronization.  ...  Conventional data stream SQL processing systems [26, 4] use incremental algorithms for evaluating continuous queries over finite windows of input data.  ... 
doi:10.1145/2882903.2915246 dblp:conf/sigmod/NikolicD016 fatcat:bnqsj45fh5fkzldf3rf6gx43ta

Incremental Data Mining Using Concurrent Online Refresh of Materialized Data Mining Views [chapter]

Mikołaj Morzy, Tadeusz Morzy, Marek Wojciechowski, Maciej Zakrzewicz
2005 Lecture Notes in Computer Science  
We present the framework for the integration of data warehouse refresh process with the maintenance of materialized data mining views.  ...  In response, relevant patterns are returned to the user for evaluation. Users usually do not achieve satisfying results immediately.  ...  In this paper we present a framework for online maintenance of a set of materialized data mining views.  ... 
doi:10.1007/11546849_29 fatcat:i72raoykcjac5o2yibc2k4oy4i

Opportunistic View Materialization with Deep Reinforcement Learning [article]

Xi Liang, Aaron J. Elmore, Sanjay Krishnan
2019 arXiv   pre-print
Such a strategy obviates the need for accurate cardinality estimation or hand-designed scoring heuristics. We focus on inner-join views and modeling effects in a main-memory, OLAP system.  ...  value of persisting certain views.  ...  DQM contains three main components: (1) An online query miner that analyzes a trace of SQL queries to identify candidate views for the current query to opportunistically materialize, (2) a RL agent that  ... 
arXiv:1903.01363v1 fatcat:hgpngvm6ubdpvgrtzd6ykpep7q

TSGV: a table-like structure-based greedy method for materialized view selection in data warehouses

Mohammad Karim SOHRABI, Hossein AZGOMI
2017 Turkish Journal of Electrical Engineering and Computer Sciences  
This table-like structure-based greedy view selection (TSGV) method is evaluated using the queries of an analytical database, and the query-processing and view maintenance costs of the selected subset  ...  Proposing an appropriate method for selecting the optimal subset of views for materialization plays an essential role in increasing the efficiency of responding to data warehouse queries.  ...  For evaluation, both methods were first tested with 60 queries and the query-processing and view maintenance costs and the total resulting cost were calculated.  ... 
doi:10.3906/elk-1608-112 fatcat:hzgk5wzidna6jgqk2vjdzsaw2e

P2P consistency support for large-scale interactive applications

Yi Hu, Laxmi N. Bhuyan, Min Feng
2012 Computer Networks  
Lookup queries for view discovery are processed by region hosts, while consistency maintenance of objects is taken by object holders.  ...  A P2P network simulator is developed to evaluate PPAct on two major types of online games: role-playing games (RPGs) and first-person shooter (FPS) games.  ...  First, each subscriber attaches a forwarding quota to its lookup query for each of its view region.  ... 
doi:10.1016/j.comnet.2012.01.012 fatcat:yjc3lihq2jgyrjdnjzfj3c2guq

When a FILTER Makes the Difference in Continuously Answering SPARQL Queries on Streaming and Quasi-Static Linked Data [chapter]

Shima Zahmatkesh, Emanuele Della Valle, Daniele Dell'Aglio
2016 Lecture Notes in Computer Science  
We are witnessing a growing interest for Web applications that i) require to continuously combine highly dynamic data stream with background data and ii) have reactivity as key performance indicator.  ...  For more information see 2  ...  Acknowledgment I would like to acknowledge the support of Soheila Dehghanzadeh and to thank her for the kind help in understanding the code base and the dataset of Acqua .  ... 
doi:10.1007/978-3-319-38791-8_17 fatcat:wjfv5i4qwnbxrl5pi4wom46oom


Justin J. Levandoski, Mohamed Sarwat, Mohamed F. Mokbel, Michael D. Ekstrand
2012 Proceedings of the 15th International Conference on Extending Database Technology - EDBT '12  
To tackle this problem, we propose RecStore, a DBMS storage engine module capable of efficient online model maintenance.  ...  RecStore maintains internal statistics and data structures aimed at providing efficient incremental updates to the recommender model, while employing an adaptive strategy for internal maintenance and load  ...  view maintenance, and query processing using views [13] .  ... 
doi:10.1145/2247596.2247608 dblp:conf/edbt/LevandoskiSME12 fatcat:ibtegdcdyfbszook6wbkxjmqa4

StreamingCube: Seamless Integration of Stream Processing and OLAP Analysis

Salman Ahmed Shaikh, Hiroyuki Kitagawa
2020 IEEE Access  
INDEX TERMS Continuous queries, cubify operator, incremental computing, materialized views, OLAP analysis, stream processing, unified framework.  ...  StreamingCube supports two types of queries, namely, continuous queries (CQs) and OLAP queries.  ...  ., their work did not discuss the materialized view maintenance for OLAP queries as we do herein.  ... 
doi:10.1109/access.2020.2999572 fatcat:jupxutdsojabxbe36azxk2pcgi

Security Criteria and Indicators of Rdbmss: a Comparative Study

Anass A. Nour
2017 IOSR Journal of Computer Engineering  
Therefore, this paper contributes to such context by conducting a comparative study intended for the security criteria and indicators of the most three famed and widely used Relational DBMSs, namely Oracle  ...  The study proposes and formulates security evaluation features derived from the standard criteria in order to accomplish such appraisal.  ...  In particular, Oracle"s unique Online Redefinition capability allows one to: Add support for parallel queries  Re-create a table to reduce fragmentation SQL Server and MySQL cannot perform online table  ... 
doi:10.9790/0661-1902035667 fatcat:tun4vev7cbeavd3ksj77o2ccya
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