Michael H. Böhlen

A foundation for representing and querying moving objects

Spatio-temporal databases deal with geometries changing over time. The goal of our work is to provide a DBMS data model and query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects. Two fundamental abstractions are moving point and moving region, describing objects for which only the time-dependent position, or position and extent, respectively, are of interest. We propose to present such time-dependent geometries as attribute data types with suitable operations, that is, to provide an abstract data type extension to a DBMS data model and query language. This paper presents a design of such a system of abstract data types. It turns out that besides the main types of interest, moving point and moving region, a relatively large number of auxiliary data types are needed. For example, one needs a line type to represent the projection of a moving point into the plane, or a “moving real” to represent the time-dependent distance of two points. It then becomes crucial to achieve (i) orthogonality in the design of the system, i.e., type constructors can be applied unifomly; (ii) genericity and consistency of operations, i.e., operations range over as many types as possible and behave consistently; and (iii) closure and consistency between structure and operations of nontemporal and related temporal types. Satisfying these goal leads to a simple and expressive system of abstract data types that may be integrated into a query language to yield a powerful language for querying spatio-temporal data, including moving objects. The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exemplifies the use of the abstract data types using SQL. The paper offers a precise and conceptually clean foundation for implementing a spatio-temporal DBMS extension.

Point-versus interval-based temporal data models

The association of timestamps with various data items such as tuples or attribute values is fundamental to the management of time varying information. Using intervals in timestamps, as do most data models, leaves a data model with a variety of choices for giving a meaning to timestamps. Specifically, some such data models claim to be point based while other data models claim to be interval based. The meaning chosen for timestamps is important it has a pervasive effect on most aspects of a data model, including database design, a variety of query language properties, and query processing techniques, e.g., the availability of query optimization opportunities. The paper precisely defines the notions of point based and interval based temporal data models, thus providing a new formal basis for characterizing temporal data models and obtaining new insights into the properties of their query languages. Queries in point based models treat snapshot equivalent argument relations identically. This renders point based models insensitive to coalescing. In contrast, queries in interval based models give significance to the actual intervals used in the timestamps, thus generally treating non identical, but possibly snapshot equivalent relations differently. The paper identifies the notion of time fragment preservation as the essential defining property of an interval based data model.

Transitioning Temporal Support in TSQL2 to SQL3

This document summarizes the proposals before the SQL3 committees to allow the addition of tables with valid-time and transactiontime support into SQL/Temporal, and explains how to use these facilities to migrate smoothly from a conventional relational system to one encompassing temporal support. Initially, important requirements to a tdiscussed. The proposal then describes the language additions necessary emporal system that may facilitate such a transition are motivated and to add valid-time support to SQL3 while fulfilling these requirements. The constructs of the language are divided into four levels, with each level adding increased temporal functionality to its predecessor. A prototype system implementing these constructs on top of a conventional DBMS is publicly available.

Querying ATSQL databases with temporal logic

We establish a correspondence between temporal logic and a subset of ATSQL, a temporal extension of SQL-92. In addition, we provide an effective translation from temporal logic to ATSQL that enables a user to write high-level queries which are then evaluated against a space-efficient representation of the database. A reverse translation, also provided in this paper, characterizes the expressive power of a syntactically defined subset of ATSQL queries.

The pq -gram distance between ordered labeled trees

When integrating data from autonomous sources, exact matches of data items that represent the same real-world object often fail due to a lack of common keys. Yet in many cases structural information is available and can be used to match such data. Typically the matching must be approximate since the representations in the sources differ. We propose pq-grams to approximately match hierarchical data from autonomous sources and define the pq-gram distance between ordered labeled trees as an effective and efficient approximation of the fanout weighted tree edit distance. We prove that the pq-gram distance is a lower bound of the fanout weighted tree edit distance and give a normalization of the pq-gram distance for which the triangle inequality holds. Experiments on synthetic and real-world data (residential addresses and XML) confirm the scalability of our approach and show the effectiveness of pq-grams.

Temporal database system implementations

Although research on temporal database systems has been active for about 20 years, implementations have not appeared until recently. This is one reason why current commercial database systems provide only limited temporal functionality. This paper summarizes extant state of the art of temporal database implementations. Rather than being very specific about each system we have attempted to provide an indication of the functionality together with pointers to additional information. It is hoped that this leads to more efforts pushing the implementation of temporal database systems in the near future.

Temporal statement modifiers

A wide range of database applications manage time-varying data. Many temporal query languages have been proposed, each one the result of many carefully made yet subtly interacting design decisions. In this article we advocate a different approach to articulating a set of requirements, or desiderata, that directly imply the syntactic structure and core semantics of a temporal extension of an (arbitrary) nontemporal query language. These desiderata facilitate transitioning applications from a nontemporal query language and data model, which has received only scant attention thus far. The paper then introduces the notion of statement modifiers that provide a means of systematically adding temporal support to an existing query language. Statement modifiers apply to all query language statements, for example, queries, cursor definitions, integrity constraints, assertions, views, and data manipulation statements. We also provide a way to systematically add temporal support to an existing implementation. The result is a temporal query language syntax, semantics, and implementation that derives from first principles. We exemplify this approach by extending SQL-92 with statement modifiers. This extended language, termed ATSQL, is formally defined via a denotational-semantics-style mapping of temporal statements to expressions using a combination of temporal and conventional relational algebraic operators.

Spatio-temporal database support for legacy applications

In areas such as finance, marketing, and property and resource management, many database applications manage spatio-temporal data. These applications typically run on top of a relational DBMS and manage spatio-temporal data either using the DBMS, which provides little support, or employ the services of a proprietary system that co-exists with the DBMS, but is separate from and not integrated with the DBMS. This wealth of applications may benefit substantially from built-in, integrated spatio-temporal DBMS support. Providing a foundation for such support is an important and substantial challenge. This paper initially defines technical requirements to a spatio-temporal DBMS aimed at protecting business investments in the existing legacy applications and at reusing personnel expertise. These requirements provide a foundation for making it economically feasible to migrate legacy applications to a spatio-temporal DBMS. The paper next presents the design of the core of a spatio-temporal extension to SQL–92, called STSQL, that satisfies the requirements. STSQL supports multiple temporal as well as spatial dimensions. Queries may “ignore” any dimension; this provides an important kind of upward compatibility with SQL–92. Queries may also view the tables in a dimensional fashion, where the DBMS provides so-called snapshot reducible query processing for each dimension. Finally, queries may view dimension attributes as if they are no different from other attributes.

Multi-dimensional Aggregation for Temporal Data

Business Intelligence solutions, encompassing technologies such as multi-dimensional data modeling and aggregate query processing, are being applied increasingly to non-traditional data. This paper extends multi-dimensional aggregation to apply to data with associated interval values that capture when the data hold. In temporal databases, intervals typically capture the states of reality that the data apply to, or capture when the data are, or were, part of the current database state. This paper proposes a new aggregation operator that addresses several challenges posed by interval data. First, the intervals to be associated with the result tuples may not be known in advance, but depend on the actual data. Such unknown intervals are accommodated by allowing result groups that are specified only partially. Second, the operator contends with the case where an interval associated with data expresses that the data holds for each point in the interval, as well as the case where the data holds only for the entire interval, but must be adjusted to apply to sub-intervals. The paper reports on an implementation of the new operator and on an empirical study that indicates that the operator scales to large data sets and is competitive with respect to other temporal aggregation algorithms.

Approximate Joins for Data-Centric XML

In data integration applications, a join matches elements that are common to two data sources. Often, however, elements are represented slightly different in each source, so an approximate join must be used. For XML data, most approximate join strategies are based on some ordered tree matching technique. But in data-centric XML the order is irrelevant: two elements should match even if their subelement order varies. In this paper we give a solution for the approximate join of unordered trees. Our solution is based on windowed pq-grams. We develop an efficient technique to systematically generate windowed pq-grams in a three-step process: sorting the unordered tree, extending the sorted tree with dummy nodes, and computing the windowed pq-grams on the extended tree. The windowed pq-gram distance between two sorted trees approximates the tree edit distance between the respective unordered trees. The approximate join algorithm based on windowed pq-grams is implemented as an equality join on strings which avoids the costly computation of the distance between every pair of input trees. Our experiments with synthetic and real world data confirm the analytic results and suggest that our technique is both useful and scalable.