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Data-science-driven development projects are increasingly gaining the attention of small and medium sized enterprises. Since SME are often lacking the necessary competencies in data science, cooperation with other companies or universities is required. The efficient handling of data is one of the main challenges in joint cross-enterprise development projects. Actual cost driver is the development of data by labeling and classifying the data by domain experts, which is very time-consuming and labor-intensive with large amounts of data. Furthermore, clearance processes also have a high potential to cause delays before data can be shared with project partners. Moreover, before the actual work can begin, it is often necessary to clean up and repair incomplete or noisy data. The concept of Data Administration Shell presented in this paper addresses the challenge of structured information sharing and information management in joint cross-enterprise engineering. The Data Administration Shell links data sets to information regarding data origin and already performed analyses including their results and program scripts. Adding relations and documentation facilitates the reuse of data sets for subsequent projects. For this purpose, the Data Administration Shell adapts the concepts serving the information sharing in the research field of manufacturing and Digital Twin. The evaluation of the Data Administration Shell was based on time-series measurement data from a production process optimization scenario. Here, the Data Administration Shell manages the data sets of time series data and facilitates the joint cross-enterprise engineering of data-driven solutions.

Integration of data stored in heterogeneous database systems is a very challenging task and it may hide several difficulties. As NoSQL databases are growing in popularity, integration of different NoSQL systems and interoperability of NoSQL systems with SQL databases become an increasingly important issue. In this paper, we propose a novel data integration methodology to query data individually from different relational and NoSQL database systems. The suggested solution does not support joins and aggregates across data sources; it only collects data from different separated database management systems according to the filtering options and migrates them. The proposed method is based on a metamodel approach and it covers the structural, semantic and syntactic heterogeneities of source systems. To introduce the applicability of the proposed methodology, we developed a web-based application, which convincingly confirms the usefulness of the novel method.

The basis of functioning of the systems from different industries are reliable data. They can be both input parameters for operation of individual modules and representation of their work results. Due to multitude of functions performed by these systems, number of necessary parameters is very big. The most convenient form for their storage and processing are database with implemented management system. This article presents how to manage database on example of IT system supporting area of logistics – SIMMAG 3D, prepared within framework of a project funded by the NCBR under the Program for Applied Research (PBS3). The aim of system is modeling of warehouse facilities in 3D, their visualization, spatial planning and evaluation of processes occurring in them, as well as placement of objects in logistics network. SIMMAG 3D system has modular structure and each module gets values from database. The flow of data between its elements was discussed. During flow of data can occur risk of access to them by unauthorized entities. This article analyzes database of SIMMAG 3D system in terms of data security against unauthorized access.

Real-time environmental monitoring, early warning and decision support systems (EMEWD) require advanced management of operational data, i.e. recent sensor data needed for the assessment of the current situation. In this paper we evaluate the suitability of four data models and corresponding database technologies – MongoDB document database, PostgreSQL relational database, Redis dictionary data server and InfluxDB time series database – to serve as an operational database for EMEWD systems. For each of the evaluated databases, we design alternative data models to represent time series data, and experimentally evaluate each of them. We also perform comparative performance evaluation of all databases, using the best model in each case. We have designed performance tests which reflect realistic conditions, using mixed workloads (simultaneous read and write operations) and queries typical for a smart levee monitoring and flood decision support system. Overall the results of the experiments allow us to answer interesting questions, such as: (1) how best to implement time series in a given data model? (2) What are the reasonable operational database volume limits? (3) What are the performance limits for different types of databases?

Objective In this study, we present an application type software which employs the Nursing Activities Score (NAS), a management tool for measuring nursing workload prospectively. Method The system was developed in two modules: WEB (controlled from an Internet browser) for data administration using Java Script; and APP (operated from a smartphone or tablet device) for data acquisition using Hypertext Preprocessor (PHP). White and black box tests were performed in the software. Results A software was developed with an interface that allows the calculation of the scale score by the same professional who provided assistance, generating reports to help nursing management. The functional test was successfully performed using the Android operational system. Conclusion The efficiency of the software was demonstrated by the functional test and the main innovations brought herein are the prospective use and the generation of management reports, which can contribute positively by improving nursing quality and safety in the intensive care unit.

Rationale and Objectives The objective of this study was to demonstrate that an intra-arterial liver therapy clinical research database system is a more workflow efficient and robust tool for clinical research than a spreadsheet storage system. The database system could be used to generate clinical research study populations easily with custom search and retrieval criteria. Materials and Methods A questionnaire was designed and distributed to 21 board-certified radiologists to assess current data storage problems and clinician reception to a database management system. Based on the questionnaire findings, a customized database and user interface system were created to perform automatic calculations of clinical scores including staging systems such as the Child-Pugh and Barcelona Clinic Liver Cancer, and facilitates data input and output. Results Questionnaire participants were favorable to a database system. The interface retrieved study-relevant data accurately and effectively. The database effectively produced easy-to-read study-specific patient populations with custom-defined inclusion/exclusion criteria. Conclusions The database management system is workflow efficient and robust in retrieving, storing, and analyzing data.

Managerial decisions are based on accurate information and in today's time raw data is produced even with a stroke of a key. Regardless of the data creating process one needs to know how the information was extracted and which pool of data was used. One important factor is time therefore we need to structure it in layers of data history in such a way that it can be analyzed, (post)process, in order to be able to retrieve valuable information. The simplest way is to use a Database Management System (DBMS), but even with such a management system we face the issue of making it a self-contained database on each version of data added. Our proposed system, a continuation of previous work, aims toward creating a database versioning system which keeps the natural dependency between data on each internal revision, a basis of security and alteration control mechanism, trend analytics, without sacrificing(within acceptable levels) speed, flexibility and the cost of implementation be kept as minimal as possible.

Many different versions of geoscience data concurrently exist in a database for different geological paradigms, source data, and authors. The aim of this study is to manage these versions in a database management system. Our data include geological surfaces, which are triangulated meshes in this study. Unlike revision/version/source control systems, our data are stored in a central database without local copies. The main contributions of this study include (1) a data model with input/output/manage functions, (2) a mesh comparison function, (3) a version merging strategy, and (4) the implementation of all of the concepts in PostgreSQL and gOcad. The software has been tested using synthetic surfaces and a simple tectonic model of a deformed stratigraphic horizon. The versions of geoscience data is managed in database systems.The data model is simple.Mesh comparison algorithm is based on the k-d tree sliding-median-widest-spread.Version merging strategy considers the conflict situations.The software is implemented in PostgreSQL and gOcad.

Urbanisation, the development of high-rise apartments and the advent of complex building structures creates unique challenges that cannot be met by 2D land and property information. These include inter-related titles and complex plans relating to (i) the land parcel and (ii) the building, both internal (indoor plans) and external attributes (roof and facade). Incorporating the third dimension into the land development cycle can potentially address such challenges by providing data that describes both the land parcel and building in 3D. This move towards 3D data administration requires the development of new 3D data processes, including 3D data sourcing, which forms the focus of this research. Following an examination of current 2D methods in land and property information registration, a framework of the requirements for sourcing 3D land and property information is suggested and potential methods are discussed. Focussing on the geometric and semantic components an evaluation of the methods is developed and applied. The results highlight methods based on photogrammetry, laser scanning, mobile mapping, Unmanned Aerial Systems (UAS) and Building Information Modelling (BIM) to source integrated 3D information for both the internal and external attributes of a building and corresponding land parcel.

In today's information society, we witness an explosive growth of the amount of information becoming available in electronic form and stored in large databases. Data mining can help in discovering knowledge. Data mining can dig out valuable information from databases in approaching knowledge discovery and improving business intelligence. In this paper, we have discussed the involvement and effect of data mining techniques on relational database systems, and how its services are accessible in databases, which tool we require to use it, with its major pros and cons in various databases. Through all this discussion we have presented how database technology can be integrated to data mining techniques.

Data resources have the characteristics of heterogeneity and differentiation; with increasing demand and the development of the scale of application systems, data resources must be constantly enriched and expanded. The traditional data resource management model has been unable to meet users' requirements of flexibility, timeliness and expansion in data resource management, which has led to major bottlenecks in system applications, cost increase and lower efficiency. We propose a method of data resource management using on-demand customizing that can be used to solve the problems stated above: on the basis of analysis of the data resources, the flexible management and customization of data resources is realized, eliminating the tight coupling between information system data storage programs and data resources, and providing transparent, flexible data resource management for enterprise applications, thus ensuring the sustainable development of information systems.

Multiple Critical Zone Observatories (CZO) have been established in recent years in the USA and other international settings to conduct collaborative research on processes that occur at and near Earth’s surface, also known as the Critical Zone (CZ). Data documentation and data sharing are two persistent problems facing the CZOs that impede the ability for cross-site comparisons and integrated analysis. In this study, a relational database was developed for CZ rock and regolith geochemical data – CZChemDB. There are a total of 24 interrelated tables in the database, each representing different aspects of CZ features. The main data group includes tables of locations, sites, samples, subsamples, preparation/treatments, laboratory-analysis and data values. The meta-data group includes tables of methods, references, and data quality. Lookup tables (variables, units, etc.) contain lists of “controlled” vocabularies. The CZChemDB is currently implemented in the MS Access database management system. It is expected to be integrated into the EarthChem portal by summer of 2011 for broader online accessibility and usability. This integration also complements the EarthChem’s global geochemistry database with CZ regolith data. The structure of the CZChemDB is simple, straightforward, and flexible so that it has potential to accommodate other chemical data collected from CZOs, such as pore fluid data. Furthermore, the development of CZChemDB represents the first attempt toward the standardization of geochemical data documentation and data sharing among CZOs. This effort will establish a model to bridge the connections between data acquisition, data management, data sharing, and data searching/discovering that are all essential but weak in terms of linkages within most geoscience research projects.

This study focuses on the development of automation platform for performing data mining on a telemetry database for computer systems. It is common for computer systems to encounter failures in an unexpected manner. It is therefore valuable to have prognostics capability for computer systems to minimize the effects of unexpected system failure. Data acquisition schemes employing telemetry techniques are considered the most effective method for collection of in-service information for computer systems. Analysis of an enormous telemetry database of high complexity must be completed before useful knowledge can be extracted. In this research, an automatic data mining platform is reported for the extraction of useful knowledge from the telemetry database. This paper describes the structure and basic theories underlying the data mining of the telemetry database. Also, an automatic computer program capable of performing database management, filtering, data analysis, and reporting is described. Some useful data generated by the platform are reported for the telemetry database.

Data Integration is becoming very important in many commercial applications and scientific research. A lot of algorithms and systems have been proposed and developed to address related issues from different aspects. Virtual database systems are well-recognized as one of the effective solutions of data integration. The existing execution modules in virtual database systems are very ineffective. MapReduce (MR) is a new computing model for parallel processing and has a good performance on large-scale data execution. In this paper, we propose a new distributed data integration system, called VDB-MR, which is based on the MapReduce technology, to efficiently integrate data from heterogeneous data sources. With VDB-MR, a unified view (i.e., a single virtual database) of multiple databases can be provided to users. We also conducted a series of experiments to evaluate VDB-MR by comparing it with an open source data integration system OGSA-DAI and two DBMSs in parallel. Experiment results show that VDB-MR significantly outperforms OGSA-DAI and the DBMSs in parallel.

One of the many challenges facing scientists who conduct simulation and analysis of biological systems is the ability to dynamically access spatially referenced climatic, soil and cropland data. Over the past several years, we have developed an Integrated Agricultural Information and Management System (iAIMS), which consists of foundation class climatic, soil and cropland databases. These databases serve as a foundation to develop applications that address different aspects of cropping systems performance and management. In this paper we present the processes and approaches involved in the development of a climatic data system designed to automatically fetch data from different web sources, consolidate the data into a centralized database, and delivery the data through a web-based interface. Climatic data are usually available via web pages or FTP sites. The exact steps to scrape data from different sources vary depending on how the data are rendered. The climatic data building process presented herein is broken down into 5 major program modules, corresponding to different phases of the process: Data Requester, Data Fetcher, Data Parser, Data Filter, and Data Explorer. The Data Requester is responsible for processing the web pages that lead to the determination of the requested weather data. The Data Fetcher is responsible for fetching weather data that is made available by the data sources based on the request from the Data Requester. The Data Parser is responsible for decompressing and parsing the contents of the original data file and saving the data to an SQL Server 2005 database. The Data Filter is responsible for data quality control and for estimating missing data and saving the filtered data. The Data Explorer is designed to provide web-based user access to the consolidated and filtered climatic data using both dropdown lists and map-based navigation. Three types of data are stored in the process: original climatic data in file format, parsed climatic data in SQL Server database, and filtered climatic data in SQL Server database. The resulting consolidated and filtered climatic database provides a common foundation that allows us to develop diversified applications that require dynamic access to near real-time data. A number of applications have been and are being developed that seamlessly access the foundation class climatic database. Collectively these applications address water conservation, crop production and management, land use suitability analysis, and bioenergy refinery site selection.

For this study, a database model of plant reliability was developed for the effective acquisition and management of plant-specific data that can be used in various applications of plant programs as well as in Probabilistic Safety Assessment (PSA). Through the development of a web-based reliability data analysis algorithm, this approach systematically gathers specific plant data such as component failure history, maintenance history, and shift diary. First, for the application of the developed algorithm, this study reestablished the raw data types, data deposition procedures and features of the Enterprise Resource Planning (ERP) system process. The component codes and system codes were standardized to make statistical analysis between different types of plants possible. This standardization contributes to the establishment of a flexible database model that allows the customization of reliability data for the various applications depending on component types and systems. In addition, this approach makes it possible for users to perform trend analyses and data comparisons for the significant plant components and systems. The validation of the algorithm is performed through a comparison of the importance measure value (Fussel–Vesely) of the mathematical calculation and that of the algorithm application. The development of a reliability database algorithm is one of the best approaches for providing systemic management of plant-specific reliability data with transparency and continuity. This proposed algorithm reinforces the relationships between raw data and application results so that it can provide a comprehensive database that offers everything from basic plant-related data to final customized data.

A new database for storing data related to the TJ-II experiment has been designed and implemented. It allows the storage of raw data not acquired during plasma shots, i.e. data collected continuously or between plasma discharges while testing subsystems (e.g. during neutral beam test pulses). This new database complements already existing ones by permitting the storage of raw data that are not classified by shot number. Rather these data are indexed according to a more general entity entitled event. An event is defined as any occurrence relevant to the TJ-II environment. Such occurrences are registered thus allowing relationships to be established between data acquisition, TJ-II control-system and diagnostic control-system actions. In the new database, raw data are stored in files on the TJ-II UNIX central server disks while meta-data are stored in Oracle tables thereby permitting fast data searches according to different criteria. In addition, libraries for registering data/events in the database from different subsystems within the laboratory local area network have been developed. Finally, a Shared Data Access System has been implemented for external access to data. It permits both new event-indexed as well as old data (indexed by shot number) to be read from a common event perspective.

Geospatial metadata have long played an important role in the management of geospatial datasets. Often employed by institutions to organise, maintain and document their geographic resources internally, metadata may also provide a vehicle for exposing marketable data assets externally when contributed to on-line geospatial exchange initiatives. In spite of the numerous benefits it affords, obstacles to the production of such geospatial surrogates are numerous. The current work proposes an approach aimed at reducing the effort associated with geospatial metadata generation through the customisation of a proprietary Geographical Information System (GIS). By coupling data preparation, management and documentation approaches with such a bespoke application, it is intended to mitigate impediments to geospatial metadata generation whilst promoting a system of data administration that safeguards the data it supports. The current prototype, implementing an extended Dublin Core geospatial profile of 23 elements, was capable of generating a total of 20 basic metadata entries. While the findings do not suggest a dispensability of human mediation in the authoring process, they do support the view that a dataset's ambient computing infrastructure has the potential to play a significant role in automating the creation of geospatial metadata.

Compared with structured data sources that are usually stored and analyzed in spreadsheets, relational databases, and single data tables, unstructured construction data sources such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our vision for data management and mining addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data mining on text-based, web-based, image-based, and network-based construction databases.

It has recently been stated that a database is an essential tool in the management of CF. The purpose of this work is to create a specific database allowing optimal performance of storage, search and retrieval functions on patients with CF. A specific database was developed using a Windev licence, for application via Microsoft supported platforms or Intranet system. The database allows real-time point of care data management of medical, investigational and administrative data. It is currently being used in the 6 Belgian reference centres. It represents a useful tool for gathering information on routine clinical and lab data, bacteriology, treatments, complications and specific outcomes for clinical and research purposes. The ongoing evolution of the database includes enhancements toward research data orientation including comparison of patient data between different centres and completeness of the National CF registry questionnaire. A complimentary copy of the software can be provided to multidisciplinary accredited CF centres worldwide upon request.

This paper discusses standards-based approaches for secure data sharing across organizations. In particular, current standards as well as standardization trends for data integration, multimedia data management, active real-time data management, data warehousing and mining, expert data management, semantic web data management, knowledge management, visualization, metadata extraction and management, and security management for data sharing are discussed. We will illustrate the ideas with an example from emergency response and public health awareness application domain.

Ever increasing environmental information demands from customers, authorities, and governmental organizations as well as new business control functions are implemented and integrated to environmental information management systems (EIMSs). These systems are often based on traditional file techniques or, more recently, on commercial database management systems (DBMSs). With a production of huge data sets and their processing in real-time applications, the needs for environmental data management have grown significantly. Numerous examples from practice of EIMSs prove that the architecture of DBMS should be open for a permanent evolution. Current trends in database development and an associated research meet these challenges. New information and communication technologies and techniques influence today's DBMSs. They include, among other things, sensor networks, stream processing, processing uncertain and imprecise data, knowledge discovery and intelligent data analysis, as well as wireless broadcast and mobile computing. Both research and practice indicate that the traditional universal DBMS architecture hardly satisfies these trends and new solutions are needed. Rather separate specialized engines connected into networks are beneficial. The paper discusses recent advances in database technologies and attempts to highlight them with respect to requirements of EIMSs.

New challenges and requirements for the management of environmental data require improved data interoperability, data integration and data sharing. This paper describes the conceptual design and implementation of the Virtual Database as an integrated environmental and landscape information system which supports corresponding techniques and methods. The Virtual Database consists of a framework for advanced Web-based retrieval, analysis, and visualization of spatially related environmental data based on the integration of distributed data repositories. Existing Internet solutions for disseminating spatial data, such as geoportals or WebGIS, essentially provide functionality for searching, mapping, publishing and limited querying. The main focus of this paper is on extending these techniques by offering spatial analysis capabilities applicable to distributed data sets. The current prototype version of the Virtual Database supports methods for applying spatial filters and performing spatial overlays. The implementation is based on modular and service-oriented software design, open specifications and open source software but also considers proprietary products where appropriate. Our results confirm the strategy of enforcing openness and modularity for reasons of extendibility and maintainability.

The US Joint Global Ocean Flux Study (JGOFS) database management system is an online, oceanographic database assembled from the research activities of the US JGOFS field and Synthesis and Modeling Program (SMP). It is based on modern, object-oriented programming, with a web-based user interface ( http://usjgofs.whoi.edu/jg/dir/jgofs ) that gives all users, regardless of the computer platform being used, equal access to the data and metadata. It is populated with an extensive set of biogeochemical data from the US JGOFS community along with the attendant metadata. This article summarizes the lessons learned that may serve as a primer for future oceanographic and earth science research programs. Good data management requires devoted resources, about 5–10% of the total cost of the program. A data management office should be established at the initiation of the program, conventions for standard methods, names, and units need to be established before the field program begins, and an agreed-to list of metadata must be collected systematically along with the data. Open and accessible data management depends upon investigators agreeing to share their data with each other, leading to more rapid scientific discovery. Data management should support data distribution and archival; interactions between the data managers and the principal investigators make the database a living database. Innovative use of commercial products in information technology can save time and money in scientific database management. Technology allows access to the database to be transparent in location and intuitive in use. Finally, the most important investments in data management are the people hired.

Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics database, gravity database, geochemistry database and remote sensing database, is very significant. We developed national important mining zone database (NIMZDB) to manage 14 national important mining zone databases to support a new round prediction of ore deposit. We found that attention should be paid to the following issues: data accuracy: integrity, logic consistency, attribute, spatial and time accuracy; management of both attribute and spatial data in the same system; transforming data between MapGIS and ArcGIS; data sharing and security; data searches that can query both attribute and spatial data. Accuracy of input data is guaranteed and the search, analysis and translation of data between MapGIS and ArcGIS has been made convenient via the development of a checking data module and a managing data module based on MapGIS and ArcGIS. Using ArcSDE, we based data sharing on a client/server system, and attribute and spatial data are also managed in the same system.

With the continuous advancement of technology many embedded and real time systems require increasingly larger amount of information to be handled and managed in a timely manner. Thus, an efficient data management mechanism is necessary and that is why there is a noticeable interest in merging standard database technology and real-time technology recently. It is very difficult to achieve guaranteed real-time database services because various components can compete for system resources. Our objective is to design an experimental real-time database system which would be suitable enough to study the most important real-time database issues. Proposed system can help us to understand the cffcct of various database components on system performance and to identify the most important factors

With increasing hydrologic and water quality data being collected for monitoring and research purposes, it has become evident that storing data in traditional spreadsheet formats are no longer a practical means of managing these large datasets. The Watershed Monitoring and Analysis Database (WMAD) was developed to ease the burden of handling watershed-scale hydrology, water quality, and meteorology data. WMAD is a relational database designed to make the management of large hydrologic datasets more efficient and less prone to manipulation errors. However, this increased efficiency comes at the cost of needing to understand relational database engines, including the ability to write queries to retrieve data. To facilitate the use of WMAD, we have applied the WMAD structure using the Microsoft Access database application. Separate WMAD applications were developed within the Access environment that allow users to access and manage data without having to understand details of the database. These applications are graphically based and allow the user to perform a series of tasks including: data import, data management, basic calculations, and data export. A constituent flux or load calculation was developed to perform flux calculations using a time-weighted algorithm. This paper highlights these applications and their tasks to assist new WMAD users in quickly becoming proficient in using WMAD for data management.

A PC Windows-based and menu-driven database management system, SARD, has been developed to provide a platform for a comprehensive use of severe accident risk information in a simple, fast and efficient way. In general, the effectiveness of the database system is strongly impacted by the availability of the technical information and the level of quality of the information. In order to meet these requirements, the present database system integrates, examines, searches, and compares the large amount of probabilistic and phenomenological information contained in the severe accident risk analysis, and can also be extended for the future use of the information for various applications. For a systematic operation and management of the database, the present database system is structured with three closely related, but distinctive modules. Each of the three modules takes charge of (a) specification of the target information for the data allocation and the retrieval, (b) automatic allocation of the severe accident risk data sets into the database, and (c) fast prediction and intelligent retrieval of the user-specified scenario-based plant response and of the symptom-based event sequence information from the database. For this, the SARD menu system provides a user-friendly interface for the user to explore the contents of the database and to obtain answers to various questions. The present database management system is very useful in a number of applications including the support of the Level 2 PSA, comparison of severe accident analysis results, training and understanding of the severe accident phenomenology, and assistance in severe accident management.

Special unique database management and processing system (Oceanbase) have been developed to work with the large sets of multivariable interdisciplinary oceanographic and environmental data. AralBase system developed to work with the Aral Sea interdisciplinary data is a variety of the OceanBase. AralBase is a special database application with the expanded functionality designed to explore large multidisciplinary oceanographic and environmental data sets. It provides many possibilities for data loading, selection, access, processing, analysis, preview and export. The AralBase has a customizing user-friendly multi-windows interface. A user can create his/her own desktops and open and arrange as many windows as he/she needs. The most valuable feature of AralBase is the integration of many service tools that allow the user to carry out almost all data processing without any additional software. At the moment, the database is loaded with the oceanographic and meteorological data provided by the participants of the Inco-Copernicus Aral-Kum Project. The OceanBase system and the AralBase, in particular, are the very flexible systems, and they can be used in any environmental and oceanographic project or institution dealing with the water environment (ocean, lakes, rivers) data. The preloaded data bases with the Data Explorer system module can be distributed among the interested scientists and experts to simplify drastically their work with data.

The increasing amount of data produced by large-scale biological experiments has highlighted the inadequacies of traditional scientific data management methods such as laboratory notebooks. Databases designed to store biological information are becoming increasingly common, but there is little guidance in the literature about the best practices of biological database design. This paper suggests best practices, and provides examples for the implementation of these practices.

In this paper an integrated geographic expert database system is presented which takes advantage of the relational database methodology combined with a geographic information system (GIS) and an expert system (ES). The GIS illustrates the data that are stored in the database after collaboration with the embedded ES. The system is fully implemented and applied to an interesting problem in apiculture.

The administration of spectral remote sensing data is a key factor for thorough, comparative analyses which take into account the entire information available to the investigator. Today, a great variety of spectrometers operate in the field, giving rise to a strong heterogeneity in data and meta-data formats. For a holistic view on these data, a spectrum database must adhere to certain principles: independence of file format, flexibility to attribute changes, establishment of relations between data, content-based search capability, common interfaces and scalability. SPECCHIO as a Web-accessible spectrum database represents a new approach that incorporates the mentioned principles and overcomes the drawbacks of file-based solutions. It is based on a relational data model and provides a prototype that accounts for the complexity of heterogeneous spectral data. SPECCHIO can be used for administration and storage purposes, as well as for online data inspection. It may also serve as a reference database for the characterization of Earth surface targets.

The rapid expansion of biomedical knowledge, reduction in computing costs, and spread of internet access have created an ocean of electronic data. The decentralized nature of our scientific community and healthcare system, however, has resulted in a patchwork of diverse, or heterogeneous, database implementations, making access to and aggregation of data across databases very difficult. The database heterogeneity problem applies equally to clinical data describing individual patients and biological data characterizing our genome. Specifically, databases are highly heterogeneous with respect to the data models they employ, the data schemas they specify, the query languages they support, and the terminologies they recognize. Heterogeneous database systems attempt to unify disparate databases by providing uniform conceptual schemas that resolve representational heterogeneities, and by providing querying capabilities that aggregate and integrate distributed data. Research in this area has applied a variety of database and knowledge-based techniques, including semantic data modeling, ontology definition, query translation, query optimization, and terminology mapping. Existing systems have addressed heterogeneous database integration in the realms of molecular biology, hospital information systems, and application portability.

Database management systems (DBMSs) form the foundation of most information systems. This study asked database developers and administrators to identify the features that are most important to them. The study finds that the features desired by respondents relate to query performance – particularly on complex queries. Respondents are also concerned about data integrity controls – particularly support for triggers, deadlock resolution, and control over data transaction logs. The study also provides insight into how respondents use the DBMS. Transaction processing and application development were primary factors. Distributed databases, administration, and general features were less important.

A database was used for data management and interprogram communication in an image processing and three-dimensional reconstruction program suite for biological bundles. The programs were modified from the MRC crystallographic package. The database server works with local and remote programs and data sets, allows simultaneous requests from multiple clients, and maintains multiple databases and data tables within them. It has built-in security for the data access. Several graphical user interfaces are available to view and/or edit data tables. In addition, FORTRAN interface and function libraries are written to communicate with image processing software. The data management overhead is inexpensive, requiring only narrow bandwidth from the network. It easily handles several data tables with over 1000 entries.

This paper outlines the historical development of data management systems in order to identify the key issues for successful systems. It identifies the need for data independence and the embedding of structural and behavioural semantics in the database as key issues in the development of modern systems. Hierarchical, Network, Relational, Object-oriented and Object-relational data management systems are reviewed. A short summary of related research is given. The paper concludes with some speculation on the future directions that database technology might take.

The design of a molecular dynamics trajectory database is presented as an example of the organization of large-scale dynamic distributed repositories for scientific data. Large scientific datasets are usually interpreted through reduced data calculated by analysis functions. This allows a database architecture in which the analyzed datasets, that are kept in addition to the raw datasets, are transferred to a database user. A flexible user interface with a well defined Application Program Interface (API) allows for a wide array of analysis functions and the incorporation of user defined functions is a critical part of the database design. An analysis function is executed only when the requested analysis result is not available from an earlier request. A prototype implementation used to gain initial practical experiences with performance and scalability is presented.

Real-time control systems are often distributed, with complex data sharing requirements. A method is proposed using a distributed real-time database, extended with active rules that react to changes in the system. This database is fully replicated and nodes communicate using only the database distribution facilities. Temporal constraints at individual nodes may be met at the expense of temporary inconsistencies in the database. This paper investigates the effects of using a rulebased representation and the replicated database as a distribution mechanism for certain classes of real-time systems. Particular attention is paid to the effect of reduced consistency, assuming eventual consistency.

There are several ways a database/analysis system can assist a scientist engaged in field research. Specifically, it can assist the scientist with accurate data collection and more rigorous data analyses. The database management portion of this system allows accurate data collection and provides easy data entry through the use of forms. In addition, it can provide the scientist with structured methods of error checking data entry and checking for implausible values and false zeros. The system can also assist the scientist with the organization of the data and provide easy methods of sorting, grouping, and selecting data for analysis. Data can then be exported to a data analysis program where the first step is data validation. A menu of analyses using examples from the coordinated research project (CRP) are demonstrated using statistical methods that test continuous and categorical data.

Ontogen Corporation and Daylight Chemical Information Systems have recently integrated a molecule data type in an object-relational database management system. The authors explain how this innovation unites chemical and biological data management into a single universal database. All chemical information storage, searching and retrieval is performed using a standard structured query language. The authors describe using rapid application development methods for quickly building an application to access the database and they demonstrate that these techniques can simplify and standardize the management of pharmaceutical data.

Many real-time systems are now being used in safety-critical applications, in which human lives or expensive machinery may be at stake. As real-time systems continue to evolve, their applications become more complex, and often require timely and predictable access to massive amounts of data. This need for advanced data management functionalities in real-time systems poses formidable intellectual and engineering challenges to database researchers and practitioners. Transactions in real-time database systems should be scheduled considering both data consistency and timing constraints. In addition, a real-time database must adapt to changes in the operating environment. The effects of scheduling decisions, and concurrency control mechanisms on real-time database systems have typically been demonstrated in a simulated environment. In this paper we present a real-time database server, called DRDB, which provides an operational platform for research in real-time distributed database issues. Current research issues involving the development of run-time estimates for use in scheduling decisions, temporal functionality, and our efforts in supporting active applications are also discussed.

This study evaluates information systems (IS) in four large Chinese state enterprises (CSEs) located in Beijing. The IS areas considered are IS planning support, IS planning methodology, data resource management, and the IS manager's role. While this study observes that these CSEs, among the largest in all China, have IS support only marginally better than that which existed 4 years earlier, it also finds evidence of slowly emerging change. An approach for evaluating IS in CSEs is proposed that focuses a lens on IS assets as an intermediate variable in benchmarking a CSE's strategic adjustment toward reform.

The complexity of genome data limits the usefulness of traditional database management systems. The highly interconnected structure of genome data can be captured in a data representation language based on the mathematical formalism of graphs. We have tailored graphs for describing genome data and have developed a database management system, called the Data Note System, for developing small databases to capture data from genome laboratories. To simplify the use of the Data Note System, a series of tools with graphical user interfaces has been developed. The system is designed to be easy to install and use by novice database developers with a minimal amount of computer expertise. We describe the tools and present examples of their use. The system consists of a storage facility, a schema editing tool to simplify the design of small databases, and three tools for data entry and querying.

Data warehousing is a method by which an organization stores its data so that the information contained in that data can be ascertained, accessed and analyzed more quickly and efficiently than would be possible when using a conventional architecture. Multidimensional databases (MDD) provide an efficient means to analytical processing, but the cost is prohibitive for many small and mid-sized organizations. This paper addresses the need for an inexpensive solution to an information management problem that can be solved by database application developers using conventional tools. A relational database management system (RDBMS) can be used to emulate a MDD and still maintain an acceptable performance level in a client server environment.

This article describes the design, implementation and testing of a set of software modules that are used for remote database access in a heterogeneous computer system. Such remote access of databases enables cost-effective use of resources, because it becomes possible to use specialized database engines for data storage and user-friendly interfaces (typically graphical) for data manipulation and database navigation. The goal of this research was to implement a client-server model using Structured Query Language functions using the sockets application programming interface. The database functions were implemented on an IBM AS/400, while the transmission control protocol/Internet protocol provided the communications support with graphical user interfaces acting as clients.

The purpose of this exploratory study was to determine the critical success factors (CSFs) of data and database administration functions. A field survey was made in 16 large corporations of information systems executives who were identified as having a key role and interest in data management issues. Several important data and database administration factors were identified. Of these, three were found to be CSFs for data administration and four were found to be CSFs for database administration. Two overlapped both areas. The overlapping factors had to be considered from both a technical and organizational perspective.

Companies are losing competitiveness because inefficient data management is becoming a bottleneck to the timely completion of process engineering projects. We describe progress on a project with the aim of solving this problem by developing a new type of data management system. British Gas plc are providing real data and feed back about the utility of our prototype systems. We outline our view of data types, databases and approaches to data management. We explain the basis of and briefly describes a version of the for managing structured numeric and unstructured document-type data that has been implemented on a central computer. We describe the concept-based approach of the POINTER System and make proposals for realising the full potential of the system design in a federal distributed data management system.

This paper discusses our experiences in using state-of-the-art software tools in teaching an advanced relational database design course (a previous graduate course in database is the prerequisite for this course), which is taken as an elective by graduate students in the Department of Computer and Information Science at Cleveland State University. As an experiment, the class material was slightly modified to include student exposure to a prototype of current software that uses expert systems information technology (IT) to automate relational database design for microcomputer users at the management level. Surprisingly, the results in terms of student interest, involvement, and inquisitiveness were far better than expected considering the minor changes to the course that were involved. Typically in the past, students in Computer and Information Science departments have approached the study of relational database design solely from the point of view of learning normalization theory. This approach was used to train graduate students to become database administrators. However, more and more the needs of today are for students to be able to understand and certainly be able to use and deal with the latest expert system software in the database area including database design. We feel that database design courses should reflect these needs.