A temporal aggregation query is an important but costly operation for applications that maintain timeevolving data (data warehouses, temporal databases, etc.). Due to the large volume of such data, performance improvements for temporal aggregation queries are critical. In this paper we examine techniques to compute temporal aggregates that include key-range predicates (range temporal aggregates). In particular we concentrate on SUM, COUNT and AVG aggregates. This problem is novel; to handle arbitrary key ranges, previous methods would need to keep a separate index for every possible key range. We propose an approach based on a new index structure called the Multiversion SB-Tree, which incorporates features from both the SB-Tree and the Multiversion B-Tree, to handle arbitrary key-range temporal SUM, COUNT and AVG queries. We analyze the performance of our approach and present experimental results that show its efficiency. 1

The rapid increase in spatio-temporal applications calls for new auxiliary indexing structures. A typical spatio-temporal application is one that tracks the behavior of moving objects through location-aware devices (e.g., GPS). Through the last decade, many spatio-temporal access methods are developed. Spatio-temporal access methods focus on two orthogonal directions: (1) Indexing the past, (2) Indexing the current and predicted future positions. In this short survey, we classify spatio-temporal access methods for each direction based on their underlying structure with a brief discussion of future research directions.

We consider in this paper how to leverage heterogeneous mobile computing capability for efficient processing of real-time range-monitoring queries. In our environment, each mobile object is associated with a resident domain and when an object moves, it monitors its spatial relationship with its resident domain and the monitoring areas inside it. An object reports its location to server whenever its movement affects any query results (i.e., crossing any query boundaries) or it moves out of its resident domain. In the first case, the server updates the affected query results accordingly while in the second case, the server determines a new resident domain for the object. This distributive approach is able to provide accurate query results and real-time monitoring updates with minimal location update and server processing costs. In addition, the new scheme allows a mobile object to negotiate a resident domain based on its computing capability. Thus, a more capable object can have a larger resident domain reducing its chance of having to request a new resident domain because of moving out of it. This feature makes the new approach highly adaptive to the heterogeneity of mobile objects. In our performance study, we compare it with an existing approach using simulation. The study shows that the new technique is many times better in reducing mobile communication and server processing costs.

In this paper we evaluate several in-memory algorithms for efficient and scalable processing of continuous range queries over collections of moving objects. Constant updates to the index are avoided by query indexing. No constraints are imposed on the speed or path of moving objects or fraction of objects that move at any moment in time. We present a detailed analysis of a grid approach which shows the best results for both skewed and uniform data. A sorting based optimization is developed for significantly improving the cache hit-rate. Experimental evaluation establishes that indexing queries using the grid index yields orders of magnitude better performance than other index structures such as R*-trees. 1

The databases of a wide range of applications, e.g., in data warehousing, store multiple states of time-evolving data. These databases contain a substantial part of now-relative data: data that became valid at some past time and remains valid until the current time. More specifically, two temporal aspects of data are frequently of interest, namely valid time, when data is true, and transaction time, when data is current in the database. The latter aspect is essential in all applications where accountability or trace-ability are required. When both aspects are captured, data is termed bitemporal. A number of indices have been devised for the efficient support of operations on time-varying data with one time dimension, but only little work, based mostly on R-trees, has addressed the indexing of two- or higher-dimensional temporal data. No indices exist that contend well with now-relative data, which leads to temporal data regions that are continuous functions of time. The paper proposes ...

Managing multiple versions of XML documents represents a critical requirement for many applications. Also, there has been much recent interest in supporting complex queries on XML data (e.g., regular path expressions, structural projections, DIFF queries). In this paper, we examine the problem of supporting efficiently complex queries on multiversioned XML documents. Our approach relies on a scheme based on durable node numbers (DNNs) that preserve the order among the XML tree nodes and are invariant with respect to updates. Using the document's DNNs various complex queries are reduced to combinations partial versio retrieval queries. We examine three indexing schemes to efficiently evaluate partial version retrieval queries in this environment. A thorough performance analysis is then presented to reveal the advantages of each scheme.

A comprehensive versioning file system creates and retains a new file version for every WRITE or other modification request. The resulting history of file modifications provides a detailed view to tools and administrators seeking to investigate a suspect system state. Conventional versioning systems do not efficiently record the many prior versions that result. In particular, the versioned metadata they keep consumes almost as much space as the versioned data. This paper examines two space-efficient metadata structures for versioning file systems and describes their integration into the Comprehensive Versioning File System (CVFS). Journal-based metadata encodes each metadata version into a single journal entry; CVFS uses this structure for inodes and indirect blocks, reducing the associated space requirements by 80%. Multiversion b-trees extend the per-entry key with a timestamp and keep current and historical entries in a single tree; CVFS uses this structure for directories, reducing the associated space requirements by 99%. Experiments with CVFS verify that its current-version performance is similar to that of non-versioning file systems. Although access to historical versions is slower than conventional versioning systems, checkpointing is shown to mitigate this effect.

Abstract—A range aggregate query returns summarized information about the points falling in a hyper-rectangle (e.g., the total number of these points instead of their concrete ids). This paper studies spatial indexes that solve such queries efficiently and proposes the aggregate Point-tree (aP-tree), which achieves logarithmic cost to the data set cardinality (independently of the query size) for two-dimensional data. The aP-tree requires only small modifications to the popular multiversion structural framework and, thus, can be implemented and applied easily in practice. We also present models that accurately predict the space consumption and query cost of the aP-tree and are therefore suitable for query optimization. Extensive experiments confirm that the proposed methods are efficient and practical. Index Terms—Database, spatial database, range queries, aggregation. 1

We examine the problem of processing temporal joins in the presence of indexing schemes. Previous work on temporal joins has concentrated on non-indexed relations which were fully scanned. Given the large data volumes created by the ever increasing time dimension, sequential scanning is prohibitive. This is especially true when the temporal join involves only parts of the joining relations (e.g., a given time interval instead of the whole timeline). Utilizing an index becomes then beneficial as it directs the join to the data of interest. We consider temporal join algorithms for three representative indexing schemes, namely a B+-tree, an R*-tree and a temporal index, the Multiversion B+-tree (MVBT). Both the B+-tree and R*-tree result in simple but not efficient join algorithms because neither index achieves good temporal data clustering. Better clustering is maintained by the MVBT through record copying. Nevertheless, copies can greatly affect the correctness and effectiveness of the join algorithms. We identify these problems and propose efficient solutions and optimizations. An extensive comparison of all index based temporal joins, using a variety of datasets and query characteristics shows that the MVBT based join algorithms are consistently faster. In particular the link-based algorithm has the most robust behavior. In our experiments it showed a ten-fold improvement over the R*-tree joins while it was between six and thirty times faster than the B+-tree joins. 1

In order to better support current and new applications, the major DBMS vendors are stepping beyond uninterpreted binary large objects, termed BLOBs, and are beginning to offer extensibility features that allow external developers to extend the DBMS with, e.g., their own data types and accompanying access methods. Existing solutions include DB2 extenders, Informix DataBlades, and Oracle cartridges. Extensible systems offer new and exciting opportunities for researchers and third-party developers alike. This paper reports on an implementation of an Informix DataBlade for the GR-tree, a new R-tree based index. This effort represents a stress test of the perhaps currently most extensible DBMS, in that the new DataBlade aims to achieve better performance, not just to add functionality. The paper provides guidelines for how to create an access method DataBlade, describes the sometimes surprising challenges that must be negotiated during DataBlade development, and evaluates the extensibility of the Informix Dynamic Server.