Abstract- We present an access method for timeslice queries that reconstructs a past state s(t) of a time-evolving collection of objects, in O(log,, n + Is(t)l/b) I/O ‘8, where Is(t)1 denotes the size of the collection at time t, n is the total number of changes in the collection’s evolution and b is the size of an I/O transfer. Changes include the addition, deletion or attribute modification of objects; they are assumed to occur in increasing time order and always affect the most current state of the collection (thus our index supports transaction-time.) The space used is 0 n/b) while the update processing is constant per change, i.e., independent of n. This is the first I I O-optimal access method for this problem using O(n/b) space and O(1) updating (in the expected amortized sense due to the use of hashing.) This performance is also achieved for interval intersection temporal queries. An advantage of our approach is that its performance can be tuned to match particular application needs (trading space for query time and vice versa). In addition, the Snapshot Index can naturally migrate data on a write-once optical medium while maintaining the same performance bounds.

. We present a new approach to implement an object bitemporal database where both valid-time and transaction-time are represented. It is based on the DataBase Version model, which allows an efficient management of object versions. This facilitates the manipulation of past events and allows a straightforward representation of branching evolution in valid-time. Keywords : bitemporal database, valid-time, transaction time, versions. Introduction In many applications time must be considered and introduced as an information stored in the database, as it has been pointed out in a huge literature (see for instance [Cen]). Among the various temporal dimensions that have been studied in databases, two of them appear particularly important, useful and moreover complementary: the valid-time of a fact which expresses the time when this fact is true in the real world, and the transaction-time which is the time of storing the fact in the database. They gave birth to bitemporal data models, s...

INDEXING PROBLEMS IN SPATIOTEMPORAL DATABASES by George N. Kollios Advisor: Vassilis Tsotras Co-Advisor: Alex Delis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (Computer Science) June 2000 Spatiotemporal databases manage spatial objects that change positions and/or extents over time. Examples include traffic surveillance data, climate and land cover data, demographic data and multimedia applications (animated movies). Since these databases are large in size, it is important to design efficient indexing schemes that can access and explore them.

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The deployment of efficient information systems has become a necessity for the success of any corporate or government operation. Critical system requirements that include maintenance of up-todate information, achievement of short transaction response times, utilization of networked computing resources, and handling of massive data volumes have resulted in the evolution of several specialized architectures for database management. Three families of such architectures that have been specifically developed to address the above requirements are: main-memory databases, client-server database and information systems, and parallel databases. By exploiting available system resources and workload characteristics, these architectures seek to optimize database processing for diverse application settings. In this chapter, we discuss the constraints as well as the implications that the various applications impose on system design, and describe key architectural features. We discuss issues in data storage and placement, query processing and optimization, concurrency control and recovery. Finally, we examine the different approaches taken by the three database architectures in order to provide efficient application support.

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.