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Spreadsheet software is often the tool of choice for ad-hoc tabular data management, processing, and visualization, especially on tiny data sets. On the other hand, relational database systems offer significant power, expressivity, and efficiency over spreadsheet software for data management, while lacking in the ease of use and ad-hoc analysis capabilities. We demonstrate D ata S pread , a data exploration tool that holistically unifies databases and spreadsheets. It continues to offer a Microsoft Excel-based spreadsheet front-end, while in parallel managing all the data in a back-end database, specifically, PostgreSQL. D ata S pread retains all the advantages of spreadsheets, including ease of use, ad-hoc analysis and visualization capabilities, and a schema-free nature, while also adding the advantages of traditional relational databases, such as scalability and the ability to use arbitrary SQL to import, filter, or join external or internal tables and have the results appear in the spreadsheet. D ata S pread needs to reason about and reconcile differences in the notions of schema, addressing of cells and tuples, and the current "pane" (which exists in spreadsheets but not in traditional databases), and support data modifications at both the front-end and the back-end. Our demonstration will center on our first and early prototype of the D ata S pread , and will give the attendees a sense for the enormous data exploration capabilities offered by unifying spreadsheets and databases.

With hundreds of millions of users, spreadsheets are one of the most important end-user applications. Spreadsheets are easy to use and allow users great flexibility in storing data. This flexibility comes at a price: users often treat spreadsheets as a poor man's database, leading to creative solutions for storing high-dimensional data. The trouble arises when users need to answer queries with their data. Data manipulation tools make strong assumptions about data layouts and cannot read these ad-hoc databases. Converting data into the appropriate layout requires programming skills or a major investment in manual reformatting. The effect is that a vast amount of real-world data is "locked-in" to a proliferation of one-off formats. We introduce FlashRelate, a synthesis engine that lets ordinary users extract structured relational data from spreadsheets without programming. Instead, users extract data by supplying examples of output relational tuples. FlashRelate uses these examples to synthesize a program in Flare. Flare is a novel extraction language that extends regular expressions with geometric constructs. An interactive user interface on top of FlashRelate lets end users extract data by point-and-click. We demonstrate that correct Flare programs can be synthesized in seconds from a small set of examples for 43 real-world scenarios. Finally, our case study demonstrates FlashRelate's usefulness addressing the widespread problem of data trapped in corporate and government formats.

Spreadsheets have become a critical data management tool, but they lack explicit relational metadata, making it difficult to join or integrate data across multiple spreadsheets. Because spreadsheet data are widely available on a huge range of topics, a tool that allows easy spreadsheet integration would be hugely beneficial for a variety of users. We demonstrate that Senbazuru, a prototype spreadsheet database management system (SSDBMS), is able to extract relational information from spreadsheets. By doing so, it opens up opportunities for integration among spreadsheets and with other relational sources. Senbazuru allows users to search for relevant spreadsheets in a large corpus, probabilistically constructs a relational version of the data, and offers several relational operations over the resulting extracted data (including joins to other spreadsheet data). Our demonstration is available on two clients: a JavaScript-rich Web site and a touch interface on the iPad. During the demo, Senbazuru will allow VLDB participants to search spreadsheets, extract relational data from them, and apply relational operators such as select and join.

Wearable sensing systems have recently enabled a variety of medical monitoring and diagnostic applications in wireless health. The need for multiple sensors and constant monitoring leads these systems to be power hungry and expensive with short operating lifetimes. We introduce a novel methodology that takes advantage of contextual and semantic properties in human behavior to enable efficient design and optimization of such systems from the data and information point of view. This, in turn, directly influences the wireless communication and local processing power consumption. We exploit intrinsic space and temporal correlations between sensor data while considering both user and system contextual behavior. Our goal is to select a small subset of sensors that accurately capture and/or predict all possible signals of a fully instrumented wearable sensing system. Our approach leverages novel modeling, partitioning, and behavioral optimization, which consists of signal characterization, segmentation and time shifting, mutual signal prediction, and a simultaneous minimization composed of subset sensor selection and opportunistic sampling. We demonstrate the effectiveness of the technique on an insole instrumented with 99 pressure sensors placed in each shoe, which cover the bottom of the entire foot, resulting in energy reduction of 72% to 97% for error rates of 5% to 17.5%.

Nowadays, most business, administration, and/or scientific databases contain both structured attributes and text attributes. We call a database that consists of both multidimensional structured data and narrative text data as multidimensional text database . Searching, OLAP, and mining such databases pose many research challenges. To enhance the power of data analysis, interesting entities and relationships can be extracted from such databases to derive heterogeneous information networks, which in turn will substantially increase the power and flexibility of data exploration in such databases. Based on our previous studies on TextCube [1], TopicCube [2], and information network analysis, such as RankClus [3] and NetClus [4], we construct iNextCube , an i nformation- N etwork- e nhanced te xt Cube . In this demo, we show the power of iNextCube in the search and analysis of two multidimensional text databases: (i) a DBLP-based CS bibliographic database, and (ii) an online news database.

Database systems have a large number of configuration parameters that control memory distribution, I/O optimization, costing of query plans, parallelism, many aspects of logging, recovery, and other behavior. Regular users and even expert database administrators struggle to tune these parameters for good performance. The wave of research on improving database manageability has largely overlooked this problem which turns out to be hard to solve. We describe iTuned, a tool that automates the task of identifying good settings for database configuration parameters. iTuned has three novel features: (i) a technique called Adaptive Sampling that proactively brings in appropriate data through planned experiments to find high-impact parameters and high-performance parameter settings, (ii) an executor that supports online experiments in production database environments through a cycle-stealing paradigm that places near-zero overhead on the production workload; and (iii) portability across different database systems. We show the effectiveness of iTuned through an extensive evaluation based on different types of workloads, database systems, and usage scenarios.

Database needs are changing, driven by the Internet and increasing amounts of scientific and sensor data. In this article, the authors propose research into several important new directions for database management systems.

It is now common for databases to contain many gigabytes, or even many terabytes, of data. Scientific experiments in areas such as high energy physics produce data sets of enormous size, while in the business sector the emergence of decision-support systems and data warehouses has led organizations to build up gigantic collections of data. Aggregate queries allow one to retrieve concise information from such a database, since they can cover many data items while returning a small result. OLAP queries, used extensively in data warehousing, are based almost entirely on aggregation [4, 16]. Aggregate queries have also been studied in a variety of settings beyond relational databases, such as mobile computing [1], global information systems [21], stream data analysis [12], sensor networks [22] and constraint databases [2].

Database systems instructors face an ongoing challenge to develop meaningful assignments for their courses. We have found that instructors can successfully use large scientific datasets in teaching a database systems course to better prepare students for real-world database systems work.

In many large companies the widespread usage of computers has led a number of different application-specific databases to be installed. As company structures evolve, boundaries between departments move, creating new business units. Their new applications will use existing data from various data stores, rather than new data entering the organization. Henceforth, the ability to make data stores interoperable becomes a crucial factor for the development of new information systems. Data interoperability may come in various degrees. At the lowest level, commercial gateways connect specific pairs of database management systems (DBMSs). Software providing facilities for defining persistent views over different databases [6] simplifies access to distant data but does not support automatic enforcement of consistency constraints among different databases. Full interoperability is achieved by distributed or federated database systems, which support integration of existing data into virtual databases (i.e. databases which are logically defined but not physically materialized). The latter allow existing databases to remain under control of their respective owners, thus supporting a harmonious coexistence of scalable data integration and site autonomy requirements [9]. Federated systems are very popular today. However, before they become marketable, many issues remain to be solved. Design issues focus on either human-centered aspects (cooperative work, including autonomy issues and negotiation procedures) or database-centered aspects (data integration, schema/database evolution). Operational issues investigate system interoperability mainly in terms of support of new transaction types, new query processing algorithms, security concerns, etc. General overviews may be found elsewhere [4, 9]. This paper is devoted to database integration, possibly the most critical issue. Simply stated, database integration is the process which takes as input a set of databases, and produces as output a single unified description of the input schemas (the integrated schema) and the associated mapping information supporting integrated access to existing data through the integrated schema. As such, database integration is also used in the process of re-engineering an exist i ng l egacy system. Database integration has attracted many diverse and diverging contributions. The purpose, and the main intended contribution of this article is to provide a clear picture of what are the approaches and the current solutions and what remains to be achieved.

Several unifications that the application development process has long needed are now occurring, due to developments in object technologies and standards. These will (gradually) change the way data intensive applications are developed, reduce databases' prominence in this process, and change data administration's goals and participants. At the same time, the database community needs to ensure that its experiences are leveraged and its concerns are met within the new methodologies and toolsets. We discuss these issues, and illustrate how they apply to a portion of the Department of Defense (DOD). We also examine things that object technology won't accomplish, and identify research problems whose solution would enable further progress.

Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse. In addition to surveying the state of the art, this paper also identifies some promising research issues, some of which are related to problems that the database research community has worked on for years, but others are only just beginning to be addressed. This overview is based on a tutorial that the authors presented at the VLDB Conference, 1996.

Database management systems address the need to store, retrieve, and manipulate large amounts of data in an organized fashion. The database held has grown tremendously in the last 25 years. It is reported that the database industry generated $7 billion in revenue in 1994 and is growing at a rate of 35% per year. Industrial and academic research have been instrumental to this growth. Theory has played an important role in defining the right abstractions and concepts, and providing a firm foundation for the field. In order to access effectively a large volume of data, one needs an abstract logical view of the data, which must be separate from the physical storage of data. The important first component of a database is therefore an abstract view of data (called the data model) and the accompanying specialized high-level language that is used to access the data. The second important component is the data structures that are used to store the data along with the algorithms to support the efficient translation from the logical to the physical world. The third important component is the mechanisms that allow the database to be accessed concurrently by many users, without violating its integrity. Theory has contributed to all three fronts, starting with what is undoubtedly the cornerstone of the area, the introduction and formal definition of the relational model by F.P. Codd (1970). It is a highly unusual compliment for theory when the major commercial products in the field have at their core a mathematically rigorous, formal model. Our primary aims in this paper will be to give a flavor of the types of problems that database theory addresses, and to review how research in the area has evolved over the years. At the end we will try to point to some topics that may be of interest to people in the FOCS community tempted to work in database theory.

The National Technical University of Athens (NTUA) is the leading Technical University in Greece. The Computer Science Division of the Electrical and Computer Engineering Department covers several fields of practical, theoretical and technical computer science and is involved in several research projects supported by the EEC, the government and industrial companies. The Knowledge and Data Base Systems (KDBS) Laboratory was established in 1992 at the National Technical University of Athens. It is recognised internationally, evidenced by its participation as a central node in the Esprit Network of Excellence IDOMENEUS. The Information and Data on Open MEdia for NEtworks of USers, project aims to coordinate and improve European efforts in the development of next-generation information environments which will be capable of maintaining and communicating a largely extended class of information in an open set of media. The KDBS Laboratory employs one full-time research engineer and several graduate students. Its infrastructure includes a LAN with several DECstation 5000/200 and 5000/240 workstations, an HP Multimedia Workstation, several PCs and software for database and multimedia applications. The basic research interests of our Laboratory include: Spatial Database Systems, Multimedia Database Systems and Active Database Systems . Apart from the above database areas, interests of the KDBS Laboratory span several areas of Information Systems, such as Software Engineering Databases, Transactional Systems, Image Databases, Conceptual Modeling, Information System Development, Temporal Databases, Advanced Query Processing and Optimization Techniques. The group's efforts on Spatial Database Systems, include the study of new data structures, storage techniques, retrieval mechanisms and user interfaces for large geographic data bases. In particular, we look at specialized, spatial data structures (R-Trees and their variations) which allow for the direct access of the data based on their spatial properties, and not some sort of encoded representation of the objects' coordinates. We study implementation and optimization techniques of spatial data structures and develop models that make performance estimation. Finally, we are investigating techniques for the efficient representation of relationships and reasoning in space. The activities on Multimedia Database Systems, include the study of advanced data models, storage techniques, retrieval mechanisms and user interfaces for large multimedia data bases. The data models under study include the object-oriented model and the relational model with appropriate extensions to support multimedia data. We are also investigating content-based search techniques for image data bases. In a different direction, we are studying issues involved in the development of multimedia front-ends for conventional, relational data base systems. In the area of Active Database Systems, we are developing new mechanisms for implementing triggers in relational databases. Among the issues involved, we address the problem of efficiently finding qualifying rules against updates in large sets of triggers. This problem is especially critical in database system implementations of triggers, where large amounts of data may have to be searched in order to find out if a particular trigger may qualify to run or not. Continuing work that started at the Foundation for Research and Technology (FORTH), Institute of Computer Science, the group is investigating reuse-oriented approaches to information systems application development. The approaches are based on a repository that has been implemented at FORTH as a special purpose object store, with emphasis on multimodal and fast retrieval. Issues of relating and describing software artifacts (designs, code, etc.) are among the topics under investigation. A new important research direction of the group is on Data Warehouses, which are seen as collections of materialized views captured over a period of time from a heterogeneous distributed information system. Issues such as consistent updates, data warehouse evolution, view reconciliation and data quality are being investigated. Research in Image Databases deals with the retrieval by image content, that uses techniques from the area of Image Processing. We are currently at early stage in this direction, having collected many segmentation and edge detection algorithms, which will be used and evaluated in images of various contents. Our work on Advanced Query Processing and Optimization Techniques includes dynamic or parametric query optimization techniques. In most database systems, the values of many important runtime parameters of the system, the data, or the query are unknown at query optimization time. Dynamic, or parametric, query optimization attempts to identify several execution plans, each one of which is optimal for a subset of all possible values of the run time parameters. In the next sections we present in detail our research efforts on the three main research areas of the KDBS Laboratory: Spatial, Multimedia and Active Databases.

In this tutorial we present the current-state-of-the art in multimedia database management systems. We discuss various issues related to semantic modeling and indexing of multimedia information. Various schemes to represent temporal synchronization requirements are explored and the current research challenges facing the multimedia database community are highlighted.

The database area has been one of those areas of computer science which have very directly been driven by application requirements; this is true today in three ways: First, the users want more application specific support from the database, and they expect the DBMS to have more semantic application knowledge. Second, users want database support for new applications which are sometimes far from the traditional database applications and introduce completely new requirements as well as the need to smoothly integrate database technology with other advanced technologies (e.g. neural nets) in one application. Finally, the embedding of databases into interactive work environments - for instance, the use of databases in cooperative environments (computer supported cooperative work) - forces the database community to reconsider some of the traditional beliefs about databases. The Database Group at Hagen University has felt these application pressures in various projects for quite a time. As a consequence the emphasis of our research has shifted from database core technology to application oriented research. Where the former research projects were mainly centered around concurrency control, recovery, distribution, and other "classical" database topics, the new research projects are concerned with the support of design environments, where design includes all activities of developing complex artifacts and, in addition, usually involves the cooperation of a variety of people. Design here includes areas like mechanical CAD, software engineering, VLSI-design, multimedia production and many others. A characteristic of design in all these areas is the use of a variety of heterogeneous tools which leads to complicated interoperability and integration issues. A key motivating factor in our work on design-environments is concurrent engineering which certainly is one of today's major industrial challenges. For computer science it opens new problems, and at the same time it requires integration of different fields. The above mentioned new requirements for databases apply in prominent ways, including the issues of managing complex data, interoperability and integration, and support of teamwork. The second research area is distributed learning environments. We are working towards systems that in future will offer, in an integrated way, computer based training and multimedia learning material, access to all sorts of information bases, communication facilities, conferencing systems, and simulation, experimentation and exercising environments. Such systems are not only important for the changing needs of university education (first of all continuing education), but also for industrial education and training systems, especially in geographically decentralized organizations. While this work is not a primarily database centered one, databases play a key role as the repositories for distributed hypermedia information. Part of our work in this second research area involves developing advanced teaching material for computer based learning in computer science, and in cooperation with other faculties, in areas like mathematics, humanities, economy, and history. The Database Group in Hagen, including the computer based learning group, consists of about 20 scientists and technicians. About half of these are financed by research and development contracts. A special unit is concerned with technology transfer and database testing for certain types of applications. Closely connected to the Database Group is the Institute for Automation, Production and Information Management. This institute was founded by three different groups of Hagen University: an economics group with a special background in PPS, an automation group with the key area robotics, and our group. This institute acts as a platform for industry projects. The research in the Hagen Database Group is not narrow focused but tries to address the multi-faceted problems of design and learning environments from different angles. The major projects are described in the following, together with the main members of the research teams. For each project, a small selection of publications is listed.

Computerized databases are essential and inseparable components of a vast majority of today's information systems. Database systems are used at all levels of management, research and production to provide uniform access and control of consistent information. Computer aided software engineering system require extensive database system support. Several industrial and academic research and development projects attempt to provide this support by using conventional database management systems or special purpose data managers.Object-oriented database management system technology provides a set of capabilities which are required by computer integrated software engineering systems. Our objective is to take advantage of this new technology and develop computer integrated software engineering systems that support complete user-defined life cycles with uniform interface and logical centralized database support. This paper presents Objectivity (DEC Object/DB) in relation to its architectural issues and shows how applications can be developed by the help of C++ interface.

This paper examines the characteristics and challenges presented by medical databases and medical information systems. It begins with a survey of medical databases/information systems. This is followed by a list of challenges for database management systems generated by the needs of these systems. It concludes with a look at some systems which address these challenges. In the context of this background information, the database community is asked to consider whether the results of database research are reaching those who are making day-to-day decisions regarding design and implementation of medical information systems.

A cornerstone of modern database systems is physical data independence, i.e., the separation of a type and its associated operations from its physical representation in memory and on storage media. Users manipulate and query data at the logical level; the DBMS translates these logical operations to operations on files, indices, records, and disks. The efficiency of these physical operations depends very much on the choice of data representations. Choosing a physical representation for a logical database is called physical database design. The number of possible choices in physical database design is very large; moreover, they very often interact with each other. We attempt to list and classify these choices and to explore their interactions. The purpose of this paper is to provide an overview of possible options to the DBMS developer and some guidance to the DBMS administrator and user. While much of our discussion will draw on the relational data model, physical database design is of even more importance for object-oriented and extensible systems. The reasons are simple: First, the number of logical data types and their operations is larger, requiring and permitting more choices for their representation. Second, the state of the art in query optimization for these systems is much less developed than for relational systems, making careful physical database design even more imperative for object-oriented database systems.

A wide variety of important data remains outside of the scope of database management systems. In recent years researchers have been making efforts in two directions: first, developing database management systems that can support non-traditional data, and secondly, offering database services for data that remains under control of autonomous (not necessarily database) systems. The purpose of this paper is to provide a brief review of these efforts.

A database model is proposed for organizing data that describes natural processes studied experimentally. Adapting concepts from object-oriented and temporal databases, this process-oriented scientific database model (POSDBM) identifies two data object types (independent and dependent variables) and two types of relationships (becomes-a and affects-a) between data objects. Successive versions of dependent variable objects are associated by the becomes-a relationship, while independent and dependent variable objects are associated by the affects-a relationship. Thus, a process can be viewed as a sequence of states (versions) of a dependent variable object whose attributes are affected over time by independent variable objects.

At the University of Massachusetts, we have been conducting research in the following database related areas: theoretical support for database system development, database programming languages, flexible concurrency control and transaction management, real-time databases, and information retrieval. The following is a summary of our research in each area.

At the Integrated Publication and Information Systems Institute (IPSI) of the GMD (Gesellschaft für Mathematik und Datenverarbeitung) database research is focused towards distributed database management systems to support the integration of heterogeneous multi-media information bases needed in an integrated publishing environment. The objective is to investigate advanced object-oriented and active data modelling concepts together with the principles of distributed data stores and data management. The unifying basis for the database research is the object-oriented data model VML developed in the project VODAK over the last four years. The data model is based on recursively defined meta classes, classes and instance hierarchies paired with a strict separation of structural and operational definitions in a polymorphic type system. This report describes the highlights of the work on the VODAK database system, the research efforts in heterogeneous database integration and the research plans of the multimedia database project as well as applications of our system in different environments such as hypertext and office automation.

The free-form, evolving, personal information that people deal with in the course of their daily activities requires more flexible data structures and data management systems than tabular data structures provide. A tool for managing personal information must conveniently handle freetextual data; allow for structure to evolve gracefully as the database grows; represent unnormalized data; and support data entry through database views . We have designed a new type of database that serves these needs—“item/category” database—and realized this design in a commercial personal computer software product named “Agenda.”

Diverse database management systems are used in large organizations. The heterogeneous distributed database system (DDS) can provide a flexible intergration of diverse databases for users and applications. This is because it allows for retrieval and update of distributed data under different data systems giving the illusion of accessing a single centralized database system.

The amount of information produced in the world increases by 30% every year and this rate will only go up. With advanced network technology, more and more sources are available either over the Internet or in enterprise intranets. Modern data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, often require integrating available data sources and providing a uniform interface for users to access data from different sources; such requirements have been driving fruitful research on data integration over the last two decades [11, 13].

Little information exists on the practice of data administration, i.e., the management of data as an enterprise resource. A survey of data administrators at predominantly large enterprises was conducted to determine the extent of the existence of data administration and to assess its effectiveness as implemented. New insight into the realities of data administration, including its real-world objectives and perceived effectiveness, is presented.

This paper attempts to examine the major control and performance issues associated with database management systems. Monitoring and evaluation of database systems is necessary in order to maintain effective security, audit, and control in a computer-based environment. A database system must perform at a level that is acceptable to both database users and the database administration team. The database system is expected to perform many different functions for the organization. It is one of the database administator's jobs to determine what performance expectations are realistic and how to control the performance of the database. If a database system is to be effective, it must meet the expectations of the user community.

A judicial systems laboratory has been established and several large-scale information management systems projects have been undertaken within the Federal Judicial Center in Washington, D.C. The newness of the court application area, together with the experimental nature of the initial prototypes, required that the system building tools be as flexible and efficient as possible for effective software design and development. The size of the databases, the expected transaction volumes, and the long-term value of the court records required a data manipulation system capable of providing high performance and integrity. The resulting design criteria, the programming capabilities developed, and their use in system construction are described herein. This database programming facility has been especially designed as a technical management tool for the database administrator, while providing the applications programmer with a flexible database software interface for high productivity. Specifically, a network-type database management system using SAIL as the data manipulation host language is described. Generic data manipulation verb formats using SAIL's macro facilities and dynamic data structuring facilities allowing in-core database representations have been developed to achieve a level of flexibility not usually attained in conventional database systems.

Database management is a new and fast growing discipline within computer science. Curriculum 68 did not include database management in its recommended courses. With the advent of large direct access secondary storage devices to hold large volumes of information referred to as a database and with the need to manage that storage efficiently and conveniently, there has developed the computer science discipline of database management. This paper describes a course in database management, including a course catalog overview, course outline, and bibliography. This paper could serve as a guideline for future ACM Curriculum Committee on Computer Science recommendations in database management.

Traditionally, application databases have been developed for efficiency of computer operations. The logical structure of the data is buried in the database schemas, application programs, and users' and programmers' minds. To manage information as a corporate resource the logical data structures must be made explicit. In this way organizational databases can be conceptually integrated. The magnitude and complexity of corporate databases makes a manual approach to this task infeasible. We propose a computer aided approach. The approach is based on extracting implicit functional dependencies from existing database schemas and forming an Entity-Relationship representation. Where the semantics of the schema are unclear, rules are applied and additional input sought from the Data Administrator. Because of its widespread use in production systems, we focus on CODASYL Network DBMSs in this paper. The approach also has applications in databases from one DBMS to another.

A second course in Database Management which stresses database design principles is described. The first course emphasizes database implementation utilizing both a relational and a CODASYL database management system. Students become familiar with the usage of a database management system as a implementation framework for databases. The second course provides the student with tools to perform logical database design and with techniques for converting logical designs into efficient physical implementations.

This research will explain the innovations in gathering and communicating data, outside the traditional role of the accounting function, which are required to adequately fulfill the information needs of all levels of management. Data Base Management Systems (DBMS) and their respective administration offer a good basis for collecting and utilizing available data, to aid the decision-making process at all levels of management. At the functional level, managers are concerned with the use of historic data. Using a DBMS, these managers will be able to obtain specific, updated summary information from the database as needed. Data Base Management, as part of an overall management information system, offers vast opportunities for more effective decision-making, installing the system that's best for your type of information, means whether or not you'll he getting appropriate data.

Many new database applications require computational and data modelling power simply not present in conventional database management systems. Developers are forced to design complex encodings of complex data into a limited set of database types, and to embed DML commands into a host programming language, a notoriously tricky and error-prone enterprise. In this paper, we describe the design and implementation of GESTALT, a system and methodology for organizing and interfacing to multiple heterogeneous, existing database systems. Application programs are written in a supported programming language (currently C and Lisp) using high-level data and control abstractions native to the language. The system is flexible in that the underlying database systems can easily be replaced/upgraded/augmented without affecting existing application programs. We also describe our experience with the system: GESTALT has been in daily operational use at MIT for over a year, supporting an information system for CAF, a research facility for the automation of semiconductor fabrication.

A new approach is proposed for the management of operating system state data. Relational database concepts are suggested for the analysis and organization of this data, and a common central service routine is described whichcould manage all shared state data. This central routine is based on the techniques currently employed by database management systems.

This paper argues for a global description, at a conceptual level, of the structure and the behavior of data, and an automatic control of the flow of data in the course of time.It presents an original model to integrate at the conceptual level, data, processes and dynamics in the design of information systems and a set of mechanisms integrated in a processor which provides the synchronized updating of the database.

This paper discusses fitting a database and database management system onto a particular research status computer constructed of a large array or network of closely-connected component computers. The findings may have application to other distributed computer architectures.

In describing a DSS architecture it is common to include a database as a component in the system. However, the DSS literature pays little attention to the data management function. Emphasis is placed on model aspects and user-system interface. In this paper we shall develop some characteristics of the data management function for a DSS. They are based on data management functions in some existing management tools as well as general developments within database theory. Finally, a particular feature of data management, data extraction, is treated. Data extraction enables loading of data into a DSS from data files external to the DSS.

Microcomputers and database management systems are two of the most rapidly expanding technologies in computer science. Because of the rapid advancements in both areas, one would expect a proliferation of microcomputer-based database systems. Although a limited number of these systems have surfaced thus far, a surprisingly small amount of information exists on the capabilities and problems of microcomputer databases. In this report we attempt to at least partially fill this information void by providing descriptions of currently available microcomputer database software packages, by determining the degree to which the limitations of today's microcomputers inhibit the realization of a complete DBMS package, and finally by projecting the effects of expected technological advances on microcomputer databases.

Normally, when an application makes use of a database, considerable resources are invested in maintaining the integrity of that database. However, in situations where use of a database may be desirable even though the normal level of resources is unavailable, a simple technique using a partitioned data file protects the database if immediate transaction recording is not essential.