In temporal document databases and temporal XML databases, temporal text-containment queries are a potential performance bottleneck. In this paper we describe how to manage documents and index structures in such databases in a way that makes temporal textcontainment querying feasible. We describe and discuss different index structures that can improve such queries. Three of the alternatives have been implemented in the V2 temporal document database system, and the performance of the index structures is studied using temporal web data. The results show that even a very simple time-indexing approach can reduce query cost by up to three orders of magnitude.

The advent of large amounts of data on the web has closed the gap between the document storage and database communities. In this paper, this work is continued by the description of the foundations for temporal document databases. We describe the V2 temporal document database, which supports storage, retrieval, and querying of temporal documents. We describe functionality and operations /operators to be supported by such systems, and more specifically we describe the architecture for management of temporal documents used in the V2 prototype. We also give some performance results from a mini-benchmark run on the V2 prototype.

Increasing amounts of data is stored in XML. In order to facilitate more efficient querying on this data, and in particular on data from several sources (for example Web sites), data can be loaded into an XML data warehouse. Often, we want to query historical document versions, or query changes between document versions. This can be facilitated by a temporal XML data warehouse (XML-DW). In this paper, we study some of the challenges we meet in an XMLDW, in particular those related to consistency, and propose how these challenges can be met. We also identify some problems that are difficult to solve automatically in a temporal XML-DW. Many of the issues described in this paper are also applicable in a broader context, for example for general Web warehouses storing on HTML web pages.

The advent of large amounts of data on the web has closed the gap between the document storage and the database communities. In this paper, this work is continued by the description of the foundations for temporal document databases. We describe functionality and operations/operators to be supported by such systems, and more specifically we describe the architecture for management of temporal documents used in the prototype of the V2 temporal document database system, which supports storage, retrieval, and querying of temporal documents. We also give some performance results from a mini-benchmark run on the V2 prototype.

Storage costs are rapidly decreasing, making it feasible to store larger amounts of data in databases. This also makes it possible to store previous versions of data in the databases, instead of only keeping the last version. Recently, the amount of data available in XML has been rapidly increasing. In this paper, we describe TeXOR, a temporal XML database system built on top of an object-relational database system. We describe the TXSQL query language used in TeXOR for querying temporal XML documents stored in the system, discuss storage alternatives for XML documents in such a system, and some details about the implementation of the current TeXOR prototype.

Support for temporal text-containment queries (query for all versions of documents that contained one or more particular words at a particular time t) is of interest in a number of contexts, including web archives, in a smaller scale temporal XML/web warehouses, and temporal document database systems in general. In the V2 temporal document database system we employ a combination of full-text indexes and variants of time indexes to perform efficient textcontainment queries. This approach was optimized for moderately large temporal document databases. However, for "extremely large databases" the index space usage of the approach could be too large. In this paper, we present a more spaceefficient solution to the problem, the architecture of the interval-based temporal text index (ITTX), we present appropriate algorithms for update and retrieval, and we discuss advantages and disadvantages of the two approaches.

In this demonstration, we present a database over complex documents, which, in addition to a structured text content, also has update information, annotations, and embedded objects. We propose a new data model called Spatiotemporal Annotation Graphs (STAG) for a database of composite digital objects and present a system that shows a query language to efficiently and effectively query such database. The particular application to be demonstrated is a database over annotated MS Word and PowerPoint presentations with embedded multimedia objects. 1.

In temporal document databases and temporal XML databases, temporal text-containment queries are a potential performance bottleneck. In this paper we describe how to manage documents and index structures in such databases in way that makes temporal text-containment querying feasible. We describe and discuss different index structures that can improve such queries. Three of the alternatives have been implemented into the V2 temporal document database system, and the performance of the index structures is studied using temporal web data. The results show that even a very simple time-indexing approach can reduce query cost by up to three orders of magnitude.

In a transaction-time temporal object database management system (TODBMS), updating an object creates a new version of the object, but the old version is still accessible. A TODBMS will store large amounts of data, and in order to provide the necessary computing power and data bandwidth, a parallel system based on a shared-nothing architecture is necessary. In order to benefit from a parallel architecture, a suitable declustering of the objects over the nodes in the system is important. In this paper, we study three low-cost declustering algorithms: 1) declustering based on the hash value of the OID of the objects, 2) range partitioning, based on the timestamp of the objects, and 3) a new hybrid algorithm, where current object versions are declustered according to the hash value of the OID, and the historical versions are range partitioned based on timestamp. In contrast to many similar studies, we study the performance with a workload including both read and update operations. We show that strategy 1 and 3 are the most scalable strategies, and that the new hybrid declustering strategy is especially suitable for low update rates, for example in geographical information systems and decision support systems with support for temporal data. However, in general declustering based on the hash value of the OID of the objects has the most stable and predictable performance.