Yong Hu

• In recent years, ”Big Data” has become an important topic in academia and industry. To handle the challenges and problems caused by Big Data, new types of data storage systems called ”NoSQL stores” (means ”Not-only- SQL”) have emerged. ”Wide-column stores” are one kind of NoSQL stores. Compared to relational database systems, wide-column stores introduce a new data model, new IRUD (Insert, Retrieve, Update and Delete) semantics with support for schema-flexibility, single-row transactions and data expiration constraints. Moreover, each column stores multiple data versions with associated time- stamps. Well-known examples are Google’s ”Big-table” and its open sourced counterpart ”HBase”. Recently, such systems are increasingly used in business intelligence and data warehouse environments to provide decision support, controlling and revision capabilities. Besides managing the current values, data warehouses also require management and processing of historical, time-related data. Data warehouses frequently employ techniques for processing changes in various data sources and incrementally applying such changes to the warehouse to keep it up-to- date. Although both incremental data warehousing maintenance and temporal data management have been the subject of intensive research in the relational database and finally commercial database products have picked up the ability for temporal data processing and management, such capabilities have not been explored systematically for today’s wide-column stores. This thesis helps to address the shortcomings mentioned above. It care- fully analyzes the properties of wide-column stores and the applicability of mechanisms for temporal data management and incremental data ware- house maintenance known from relational databases, extends well-known approaches and develops new capabilities for providing equivalent support in wide-column stores.