In a variety of applications, we need to keep track of the development of a data set over time. For maintaining and querying these multiversion data efficiently, external storage structures are an absolute necessity. We propose a multiversion B-tree that supports insertions and deletions of data items at the current version and range queries and exact match queries for any version, current or past. Our multiversion B-tree is asymptotically optimal in the sense that the time and space bounds are asymptotically the same as those of the (single-version) B-tree in the worst case. The technique we present for transforming a (single-version) Btree into a multiversion B-tree is quite general: it applies to a number of hierarchical external access structures with certain properties directly, and it can be modified for others.

We define formal notions of temporal domain and temporal database, and use them to survey a wide spectrum of temporal query languages. We distinguish between an abstract temporal database and its concrete representations, and accordingly between abstract and concrete temporal query languages. We also address the issue of incomplete temporal information. 1 Introduction A temporal database is a repository of temporal information. A temporal query language is any query language for temporal databases. In this paper we propose a formal notion of temporal database and use this notion in surveying a wide spectrum of temporal query languages. The need to store temporal information arises in many computer applications. Consider, for example, records of various kinds: financial [37], personnel, medical [98], or judicial. Also, monitoring data, e.g., in telecommunications network management [4] or process control, has often a temporal dimension. There has been a lot of research in temporal dat...

The engineers who analyze traffic on high bandwidth networks must filter and aggregate either recorded traces of network packets or live traffic from the network itself. These engineers perform operations similar to database queries, but cannot use conventional data managers because of performance concerns and a semantic mismatch between the analysis operations and the operations supported by commercial DBMSs. Traffic analysis does not require fast random access, transactional update, or relational joins. Rather, it needs fast sequential access to a stream of traffic records and the ability to filter, aggregate, define windows, demultiplex, and remultiplex the stream. Tribeca is an extensible, stream-oriented DBMS designed to support network traffic analysis. It combines ideas from temporal and sequence databases with an implementation optimized for databases stored on high speed ID-1 tapes or arriving in real time from the network. The paper describes Tribeca's query language, executo...

Abstract—An efficient multiversion access structure for a transaction-time database is presented. Our method requires optimal storage and query times for several important queries and logarithmic update times. Three version operations}inserts, updates, and deletes}are allowed on the current database, while queries are allowed on any version, present or past. The following query operations are performed in optimal query time: key range search, key history search, and time range view. The key-range query retrieves all records having keys in a specified key range at a specified time; the key history query retrieves all records with a given key in a specified time range; and the time range view query retrieves all records that were current during a specified time interval. Special cases of these queries include the key search query, which retrieves a particular version of a record, and the snapshot query which reconstructs the database at some past time. To the best of our knowledge no previous multiversion access structure simultaneously supports all these query and version operations within these time and space bounds. The bounds on query operations are worst case per operation, while those for storage space and version operations are (worst-case) amortized over a sequence of version operations. Simulation results show that good storage utilization and query performance is obtained. Index Terms—Transaction-time database, multidimensional data, access methods, data structures, indexing, I/O complexity.

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 standard relation has two dimensions: attributes and tuples. A temporal relation contains two additional orthogonal time dimensions, namely, valid time and transaction time. Valid time records when facts are true in the modeled reality, and transaction time records when facts are stored in the temporal relation. Although, in general, there are no restrictions between the valid time and transaction time associated with each fact, in many practical applications, the valid and transaction times exhibit more or less restricted interrelationships that define several types of specialized temporal relations. The paper examines five different areas where a variety of types of specialized temporal relations are present. In application systems with multiple, interconnected temporal relations, multiple time dimensions may be associated with facts as they flow from one temporal relation to another. For example, a fact may have an associated transaction time indicating when it was stored in a previous temporal relation. The paper investigates several aspects of the resulting generalized temporal relations, including the ability to query a predecessor relation from a successor relation. The presented framework for generalization and specialization allows researchers as well as database and system designers to precisely characterize, compare, and thus better understand temporal relations and the application systems in which they are embedded. The framework’s comprehensiveness and its use in understanding temporal relations are demonstrated by placing previously proposed temporal data models within the framework. The practical relevance of the defined specializations and gener-alizations is illustrated by sample realistic applications in which they occur. The additional semantics of specialized relations are especially useful for improving the performance of query processing.

Abstract-The size of temporal databases and the semantics cases are subsequently investigated: 1) Dynamic structures for of temporal queries pose challenges for the design of efhcient indexing methods. The primary issues that affect the design of indexing methods are examined, and propose several structures and algorithms for specific cases. Indexing methods for timebased queries are developed, queries on the surrogate or timesurrogate and time indexing (ST); 2) Static and dynamic partitioning algorithms for the time-line in the context of temporal attribute and time indexing; and 3) Time-indexing for append-only database. In all the designs, the focus is on invariant key and time, and temporal attribute and time. In the the role of the time attribute. latter case, several methods are presented that partition the timeline, in order to balance the distribution of tuple-pointers within the index. The methods are analyzed against alternatives, and present appropriate empirical results. Index Terms-Indexing, physical organization, query processing, searching, temporal databases. The paper is organized as follows. In Section II, we discuss the relational representation of data in the temporal context, followed by a framework for analyzing the physical design of a temporal database. In Section III, we introduce the APtree, which is designed for time-based query operations on an append-only database, and is subsequently incorporated into our surrogate-time index of Section IV. In Section V, we I.

. High-performance transaction system applications typically insert rows in a History table to provide an activity trace; at the same time the transaction system generates log records for purposes of system recovery. Both types of generated information can benefit from efficient indexing. An example in a well-known setting is the TPC-A benchmark application, modified to support efficient queries on the History for account activity for specific accounts. This requires an index by account-id on the fast-growing History table. Unfortunately, standard disk-based index structures such as the B-tree will effectively double the I/O cost of the transaction to maintain an index such as this in real time, increasing the total system cost up to fifty percent. Clearly a method for maintaining a real-time index at low cost is desirable. The Log-Structured Merge-tree (LSM-tree) is a disk-based data structure designed to provide low-cost indexing for a file experiencing a high rate of record inserts ...

Numerous applications such as stock market or medical informa-tion systems require that both historical and current data be logical-ly integrated into a temporal database. The underlying access method must support different forms of “time-travel ” queries, the migration of old record versions onto inexpensive archive media, and high insert and update rates. This paper introduces a new ac-cess method for transaction-time temporal data, called the Log-structured History Data Access Method (LHAM) that meets these demands. The basic principle of LHAM is to partition the data into successive components based on the timestamps of the record ver-sions. Components are assigned to different levels of a storage hier-archy, and incoming data is continuously migrated through the hierarchy. The paper discusses the LHAM concepts, including concurrency control and recovery, our full-fledged LHAM imple-mentation, and experimental performance results based on this im-plementation. A detailed comparison with the TSB-tree, both ana-lytically and based on experiments with real implementations, shows that LHAM is highly superior in terms of insert performance while query performance is in almost all cases at least as good as for the TSB-tree; in many cases it is much better, I

. We propose an asymptotically optimal multiversion B-tree. In our setting, insertions and deletions of data items are allowed only for the present version, whereas range queries and exact match queries are allowed for any version, present or past. The technique we present for transforming a (usual single version) B-tree into a multiversion B-tree is more general: it applies to a number of spatial and non-spatial hierarchical external access structures with certain properties directly, and it can be modified for others. For the B-tree and several other hierarchical external access structures, multiversion capabilities come at no extra cost, neither for storage space nor for runtime, asymptotically in the worst case. The analysis of the behavior of the multiversion B-tree shows that the constant loss of efficiency is low enough to make our suggestion not only a theoretical, but also a practical one. 1 Introduction The importance of maintaining data not only in their latest version, but ...