Completeness as one of the four major dimensions of data quality is a pervasive issue in modern databases. Although data imputation has been studied extensively in the literature, most of the research is focused on inference-based approach. We propose to harness Web tables as an external data source to effectively and efficiently retrieve missing data while taking into account the inherent uncertainty and lack of veracity that they contain.

In this paper, we discuss the state of the art and current trends in designing and optimizing ETL workflows. We explain the existing techniques for: (1) constructing a conceptual and a logical model of an ETL workflow, (2) its corresponding physical implementation, and (3) its optimization, illustrated by examples. The discussed techniques are analyzed w.r.t. their advantages, disadvantages, and challenges in the context of metrics such as autonomous behavior, support for quality metrics, and support for ETL activities as user-defined functions. We draw conclusions on still open research and technological issues in the field of ETL. Finally, we propose a theoretical ETL framework for ETL optimization.

One of the important research and technological problems in data warehousing is the optimization of star queries. So far, most of the research focused on optimizing such queries by means of join indexes and bitmap join indexes. In this paper we propose an index, called Time-HOBI, for optimizing star queries and computing aggregates along dimension hierarchies. Time-HOBI, created on a dimension hierarchy, is composed of: (1) a hierarchically organized bitmap index (HOBI), one bitmap index for one dimension level and (2) a time index (TI) that implicitly encodes time in every dimension. HOBI allows to quickly search for fact rows that fulfill selection criteria. With the support of TI joining a fact table with the Time dimension is avoided. Time-HOBI was implemented and evaluated experimentally on a real dataset, coming from the biggest East-European Internet auction platform Allegro.pl. The experiments show that Time-HOBI offers a promising star query performance.

In this paper we present a technique of compressing bitmap indexes for application in data warehouses. The developed compression technique, called Run-Length Huffman (RLH), is based on the run-length encoding and on the Huffman encoding. RLH was implemented and experimentally compared to the well known Word Aligned Hybrid bitmap compression technique that has been reported to provide the shortest query execution time. The experiments discussed in this paper show that RLH offers shorter query response times than WAH, for certain cardinalities of indexed attributes. Moreover, bitmaps compressed with RLH are smaller than corresponding bitmaps compressed with WAH. Additionally, we propose a modified RLH, called RLH-1024, which is designed to better support bitmap updates.

A data warehouse (DW) is fed with data that come from external data sources that are production systems. External data sources, which are usually autonomous, often change not only their content but also their structure. The evolution of external data sources has to be reflected in a DW, that uses the sources. Traditional DW systems offer a limited support for handling dynamics in their structure and content. A promising approach to handling changes in DW structure and content is based on a multiversion data warehouse. In such a DW, each DW version describes a schema and data at certain period of time or a given business scenario, created for simulation purposes. In order to appropriately analyze multiversion data, an extension to a traditional SQL language is required. In this paper we propose an approach to querying a multiversion DW. To this end, we extended a SQL language and built a multiversion query language interface with functionality that allows: (1) expressing queries that address several DW versions and (2) presenting their results annotated with metadata information.

The application of materialised object-oriented views in object-relational data warehousing systems is promising. In this paper we propose a novel technique for the materialisation of method results in object-oriented views, called hierarchical materialisation. When an object used to materialise the result of method m is updated, then m has to be recomputed. This recomputation can use unaffected intermediate materialised results of methods called from m, thus reducing a recomputation time. The hierarchical materialisation technique was implemented and evaluated by a number of experiments concerning methods without input arguments as well as methods with input arguments. The results showed that hierarchical materialisation reduces method recomputation time. Moreover, materialising methods with input arguments of narrow discrete domains introduces only a small time overhead.