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A new issue that arises in modern applications involves the efficient manipulation of (static or moving) spatial objects, and the relationships among them. As a result, modern database systems should be able to efficiently support that type of data. Towards this goal, appropriate extensions of multidimensional access methods can be exploited in order to index and retrieve spatiotemporal objects, satisfying users' demands. This paper introduces the basic specifications such a spatiotemporal index structure should follow, evaluates existing proposals with respect to the above specifications, and illustrates issues of interest involving object representation, query processing, and index maintenance.

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.

This report presents a survey on the topic of indexing techniques for temporal databases assuming the relational data model as underlying framework. Temporal databases are to preserve the history of tuples, and hence relations, and therefore tuples are never deleted, but versioned instead. Versioning, on the other hand, makes the databases grow unreasonably, and then some part of the database may be migrated to a lower level, slower but less expensive, storage. We review several indexing techniques that are to provide some means to index such temporal databases and that to some extent cover the issues mentioned, focus is made on the used data structures. A comparison between these techniques is also made. Finally, we present potential data structures aimed to support truly bitemporal database indexing.

Temporal databases assume a single line of time evolution. In other words, they support timeevolving data. However there are applications which require the support of temporal data with branched time evolution. With new branches created as time proceeds, branched and temporal data tends to increase in size rapidly, making the need for efficient indexing crucial. We propose a new (paginated) access method for branched and temporal data: the BT-tree. The BT-tree is both storage efficient and access efficient. Wehaveimplemented the BT-tree and performance results confirm these properties.

Performance of the data warehouse depends on physical design. Index selection and storage of multidimensional data bases are important activities of physical designing process. Conventional indexing techniques such as bitmaps, B-trees and hash based indexing systems need large storage space for storing indexes along with data itself. Spelling variants, misspellings and transliteration differences are source of uncertainty in data with in the databases. Misspelled and distorted key values are also hard to map in present indexing systems. In this paper neural network based physical design is suggested, a class of artificial neural network known as self-organizing net is used for indexing data warehouse at physical level. Indexes of active neurons will be used for generating indexes for the data values. In conventional indexing techniques every key value is mapped to a specific point in space, while in neural network based database indexing system, every key value is mapped to a region in space. This region is a class to which the key values of similar type belong. Indexes generated through this method used optimal space for storage, as only final weight matrices after training of neurons are stored. Self-organizing net based indexing is very robust as distorted key values get indexed to right classes. Accuracy of our selforganizing net based indexing system in mapping key values with distorted keys is found to be high.

Performance of the data warehouse depends on physical design. Index selection and storage of multidimensional data bases are important activities of physical designing process. Conventional indexing techniques such as bitmaps, B-trees and hash based indexing systems need large storage space for storing indexes along with data itself. Spelling variants, misspellings and transliteration differences are source of uncertainty in data with in the databases. Misspelled and distorted key values are also hard to map in present indexing systems. In this paper neural network based physical design is suggested, a class of artificial neural network known as self-organizing net is used for indexing data warehouse at physical level. Indexes of active neurons will be used for generating indexes for the data values. In conventional indexing techniques every key value is mapped to a specific point in space, while in neural network based database indexing system, every key value is mapped to a region in space. This region is a class to which the key values of similar type belong. Indexes generated through this method used optimal space for storage, as only final weight matrices after training of neurons are stored. Self-organizing net based indexing is very robust as distorted key values get indexed to right classes. Accuracy of our selforganizing net based indexing system in mapping key values with distorted keys is found to be high.

This paper compares different indexing techniques proposed for supporting efficient access to temporal data. The comparison is based on a collection of important performance criteria, including the space consumed, update processing, and query time for representative queries. The comparison is based on worst-case analysis, hence no assumptions on data distribution or query frequencies are made. When a number of methods have the same asymptotic worst-case behavior, features in the methods that affect average case behavior are discussed. Additional criteria examined are the pagination of an index, the ability to cluster related data together, and the ability to efficiently separate old from current data (so that larger archival storage media such as write-once optical disks can be used). The purpose of the paper is to identify the difficult problems in accessing temporal data and describe how the different methods aim to solve them. A general lower bound for answering basic temporal queries is also introduced.