Materialisierte views in verteilten key-value stores

Distributed key-value stores have become the solution of choice for warehousing large volumes of data. However, their architecture is not suitable for real-time analytics. To achieve the required velocity, materialized views can be used to provide summarized data for fast access. The main challenge then, is the incremental, consistent maintenance of views at large scale. Thus, we introduce our View Maintenance System (VMS) to maintain SQL queries in a data-intensive real-time scenario.Verteilte key-value stores sind ein Typ moderner Datenbanken um große Mengen an Daten zu verarbeiten. Trotzdem erlaubt ihre Architektur keine analytischen Abfragen in Echtzeit. Materialisierte Views können diesen Nachteil ausgleichen, indem sie schnellen Zuriff auf Ergebnisse ermöglichen. Die Herausforderung ist dann, das inkrementelle und konsistente Aktualisieren der Views. Daher präsentieren wir unser View Maintenance System (VMS), das datenintensive SQL Abfragen in Echtzeit berechnet

Incremental Maintenance of a Materialized view in Data Warehousing : An Effective Approach

A view is a derived relation defined in terms of base relations. A view can be materialized by storing its extent in the database. An index can be made of these views and access to materialized view is much faster that recomputing the view from scratch. A Data Warehouse stores large amount of information collected from a different data sources. In order to speed up query processing, warehouse usually contains a large number of materialized views. When the data sources are updated, the views need to be updated. The process of keeping view up to date called as materialize view maintenance. Accessing base relations for view maintenance can be difficult, because the relations may be being used by users. Therefore materialize view maintenance in data warehousing is an important issue. For these reasons, the issue of self-maintainability of the view is an important issue in data warehousing. In this paper we have shown that a materialized view can be maintained without accessing the view itself by materializing additional relations at the data warehouse site. We have developed a cost effective approach to reduce the burden of view maintenance and also proved that proposed approach is optimum as compared to other approaches. Here incremental evaluation algorithm to compute changes to materialized views in relational is presented

Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

Clustering-Based Materialized View Selection in Data Warehouses

Materialized view selection is a non-trivial task. Hence, its complexity must be reduced. A judicious choice of views must be cost-driven and influenced by the workload experienced by the system. In this paper, we propose a framework for materialized view selection that exploits a data mining technique (clustering), in order to determine clusters of similar queries. We also propose a view merging algorithm that builds a set of candidate views, as well as a greedy process for selecting a set of views to materialize. This selection is based on cost models that evaluate the cost of accessing data using views and the cost of storing these views. To validate our strategy, we executed a workload of decision-support queries on a test data warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when storage space is limited

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft