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.

The goal of the VIMSYS project is to create an information management system that can retrieve images or parts of images in addition to textual data. The system allows users to incrementally formulate a query starting from semantic pictorial objects. 11 allows incompletely specified and similarity-based queries. This paper presents a new layered data model and the design of the query processing unit for this system. The data model is a combination of object-oriented and functional models, and permits multiple representations of image entities. The knowledge module guides the user to progrcssively refine similarity-based queries and to query by defining new semantic objects and composing its attributes. Design considerations for the access structure of the system are also discussed, alongwith some of the problems of creating efficient mechanisms of access. 2

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.

Many applications require the ability to manipulate sequences of data. We motivate the importance of sequence query processing, and present a framework for the optimization of sequence queries based on several novel techniques. These include query transformations, optimizations that utilize meta--data, and caching of intermediate results. We present a bottom-up algorithm that generates an efficient query evaluation plan based on cost estimates. This work also identifies a number of directions in which future research can be directed. 1 Introduction Many real life applications manipulate data that is inherently sequential. Such data is logically viewed and queried in terms of a sequence abstraction and is often physically stored as a sequence. Databases should (a) allow sequences to be queried in a declarative manner, utilizing the ordered semantics of the data, and (b) take advantage of the opportunities available for query optimization. Relational databases are inadequate in this re...

XML documents are typically queried with a combination of value search and structure search. While querying by values can leverage traditional database technologies, evaluating structural relationship, specifically parent-child or ancestor-descendant relationship, between XML element sets has imposed a great challenge on efficient XML query processing. This paper proposes XR-tree, namely, XML Region Tree, which is a dynamic external memory index structure specially designed for strictly nested XML data. The unique feature of XR-tree is that, for a given element, all its ancestors (or descendants) in an element set indexed by an XRtree can be identified with optimal worst case I/O cost. We then propose a new structural join algorithm that can evaluate the structural relationship between two XR-tree indexed element sets by effectively skipping ancestors and descendants that do not participate in the join. Our extensive performance study shows that the XR-tree based join algorithm significantly outperforms previous algorithms. 1.

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.

Although “now ” is expressed in SQL as CURRENT_TIMESTAMP within queries, this value cannot be stored in the database. However, this notion of an ever-increasing current-time value has been reflected in some temporal data models by inclusion of database-resident variables, such as “now”, “until-changed, ” “�, ” “@, ” and “–”. Time variables are very desirable, but their use also leads to a new type of database, consisting of tuples with variables, termed a variable database. This article proposes a framework for defining the semantics of the variable databases of the relational and temporal relational data models. A framework is presented because several reasonable meanings may be given to databases that use some of the specific temporal variables that have appeared in the literature. Using the framework, the article defines a useful semantics for such databases. Because situations occur where the existing time variables are inadequate, two new types of modeling entities that address these shortcomings, timestamps that we call now-relative and now-relative indeterminate, are introduced and defined within the framework. Moreover, the article provides a foundation, using algebraic

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Modern database applications show a growing demand for efficient and dynamic management of intervals, particularly for temporal and spatial data or for constraint handling. Common approaches require the augmentation of index structures which, however, is not supported by existing relational database systems. By design, the new Relational Interval Tree (RI-tree) employs built-in indexes on an as-they-are basis and is easy to implement. Whereas

. We examine new I/O-efficient techniques for indexing problems in constraint and temporal data models. We present algorithms for these problems that are considerably simpler than previous solutions. Our solutions are unique in the sense that they only use B + -trees rather than special-purpose data structures. Indexing for many general constraint data models can be reduced to interval intersection. We present a new algorithm for this problem using a query-time/space tradeoff, which achieves the optimal query time O(log B n + t=B) I/O's in linear space O(n=B) using B + -trees. (Here, n is the number of intervals, t the number of intervals in the output of a query, and B the disk block size.) It is easy to update this data structure, but small worst-case bounds do not seem possible. Previous approaches have achieved these bounds but are fairly complex and rely mostly on reducing the interval intersection problem to special cases of two-dimensional search. Some of them c...