Alex Russakovsky - Academia.edu (original) (raw)

Papers by Alex Russakovsky

www.truviso.com Modern data analysis applications driven by the Network Effect are pushing tradit... more www.truviso.com Modern data analysis applications driven by the Network Effect are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. To address this problem, we advocate an integrated query processing approach that runs SQL continuously and incrementally over data before that data is stored in the database. Continuous Analytics technology is seamlessly integrated into a full-function database system, creating a powerful and flexible system that can run SQL over tables, streams, and combinations of the two. A continuous analytics system can run many orders of magnitude more efficiently than traditional store-first-query-later technologies. In this paper, we describe the Continuous Analytics approach and outline some of the key technical arguments behind it. 1.

Modern data analysis applications driven by the Network Effect are pushing traditional database a... more Modern data analysis applications driven by the Network Effect are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. To address this problem, we advocate an integrated query processing approach that runs SQL continuously and incrementally over data before that data is stored in the database. Continuous Analytics technology is seamlessly integrated into a full-function database system, creating a powerful and flexible system that can run SQL over tables, streams, and combinations of the two. A continuous analytics system can run many orders of magnitude more efficiently than traditional store-first-query-later technologies. In this paper, we describe the Continuous Analytics approach and outline some of the key technical arguments behind it.

In a data warehouse, real-world activities can trigger changes to dimensions and their hierarchic... more In a data warehouse, real-world activities can trigger changes to dimensions and their hierarchical structure. E.g., organizations can be reorganized over time causing changes to reporting structure. Product pricing changes in select markets can result in changes to bundled options in those markets. Much of the previous work on trend analysis on data warehouses has mainly focused on efficient evaluation

Conference on Innovative Data Systems Research, 2009

Modern data analysis applications driven by the Network Effect are pushing traditional database a... more Modern data analysis applications driven by the Network Effect are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. To address this problem, we advocate an integrated query processing approach that runs SQL continuously and incrementally over data before that data is stored in the database. Continuous

Conference on Innovative Data Systems Research, 2009

Modern data analysis applications driven by the Network Effect are pushing traditional database a... more Modern data analysis applications driven by the Network Effect are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. To address this problem, we advocate an integrated query processing approach that runs SQL continuously and incrementally over data before that data is stored in the database. Continuous

www.truviso.com Modern data analysis applications driven by the Network Effect are pushing tradit... more www.truviso.com Modern data analysis applications driven by the Network Effect are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. To address this problem, we advocate an integrated query processing approach that runs SQL continuously and incrementally over data before that data is stored in the database. Continuous Analytics technology is seamlessly integrated into a full-function database system, creating a powerful and flexible system that can run SQL over tables, streams, and combinations of the two. A continuous analytics system can run many orders of magnitude more efficiently than traditional store-first-query-later technologies. In this paper, we describe the Continuous Analytics approach and outline some of the key technical arguments behind it. 1.

Modern data analysis applications driven by the Network Effect are pushing traditional database a... more Modern data analysis applications driven by the Network Effect are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. To address this problem, we advocate an integrated query processing approach that runs SQL continuously and incrementally over data before that data is stored in the database. Continuous Analytics technology is seamlessly integrated into a full-function database system, creating a powerful and flexible system that can run SQL over tables, streams, and combinations of the two. A continuous analytics system can run many orders of magnitude more efficiently than traditional store-first-query-later technologies. In this paper, we describe the Continuous Analytics approach and outline some of the key technical arguments behind it.

In a data warehouse, real-world activities can trigger changes to dimensions and their hierarchic... more In a data warehouse, real-world activities can trigger changes to dimensions and their hierarchical structure. E.g., organizations can be reorganized over time causing changes to reporting structure. Product pricing changes in select markets can result in changes to bundled options in those markets. Much of the previous work on trend analysis on data warehouses has mainly focused on efficient evaluation

Conference on Innovative Data Systems Research, 2009

Modern data analysis applications driven by the Network Effect are pushing traditional database a... more Modern data analysis applications driven by the Network Effect are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. To address this problem, we advocate an integrated query processing approach that runs SQL continuously and incrementally over data before that data is stored in the database. Continuous

Conference on Innovative Data Systems Research, 2009

Modern data analysis applications driven by the Network Effect are pushing traditional database a... more Modern data analysis applications driven by the Network Effect are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. To address this problem, we advocate an integrated query processing approach that runs SQL continuously and incrementally over data before that data is stored in the database. Continuous