A query optimization trategy for implementing multi dimensional model in spatial database system

Spatial Data Warehouses (SDWs) combine spatial databases and data warehouses allowing analysis of historical data represented in a space. This data can be queried using Spatial On-Line Analytical Processing (SOLAP) systems. SDW and SOLAP systems are emerging areas that raise several research issues. In this paper, we refer to different problems existing in SDWs that motivated us to propose the MultiDimER model - a conceptual multidimensional model able to express users' requirements for SDW and SOLAP applications. We present also different research directions that are important to consider to provide satisfactory solutions for SDW and for SOLAP systems.

Spatial data warehouses (SDW) and spatial OLAP (SOLAP) systems are well-known business intelligence technologies that aim to support a multidimensional and online analysis for a large volume of geo-referenced datasets. SOLAP systems are already used in the context of natural hazards for analyzing sensor data and experts' measurements. Recently, new data gathering tools coined as volunteered geographic information systems (VGI) have been adopted especially by non-expert users. Hence, (spatial) application development is facing a new challenge, which is the integration of expert-oriented data with citizen-provided data. In this paper, we propose a new generic spatio-multidimensional model based on the question/answer risk evaluation model that allows the integration of VGI data with classical SDW and SOLAP systems for the online analysis of natural hazards monitored by volunteers.

Spatial data warehouses (SDW) and spatial OLAP (SOLAP) systems are well-known business intelligence technologies that aim to support a multidimensional and online analysis for a large volume of geo-referenced datasets. SOLAP systems are already used in the context of natural hazards for analyzing sensor data and experts' measurements. Recently, new data gathering tools coined as volunteered geographic information systems (VGI) have been adopted especially by non-expert users. Hence, (spatial) application development is facing a new challenge, which is the integration of expert-oriented data with citizen-provided data. In this paper, we propose a new generic spatio-multidimensional model based on the question/answer risk evaluation model that allows the integration of VGI data with classical SDW and SOLAP systems for the online analysis of natural hazards monitored by volunteers.

The design of Spatial OLAP (SOLAP) applications consists of (i) Spatial Data Warehouse (SDW) model design and (ii) SOLAP visualization definition because a specific set of understandable and readable cartographic visualizations corresponds to a particular type of SOLAP query. Unfortunately few works investigate geovisualization issues in SOLAP systems and propose new methodologies to visualize spatio-temporal data, and no works investigate tools for readable SOLAP cartographic displays. Moreover, some works propose ad-hoc methodologies for DWs and SDWs exclusively based on data and user analysis requirements. Therefore, we present in this paper (i) a new geovisualization methodology for SOLAP queries that yields readable maps and (ii) a new prototyping design methodology for SOLAP applications that accounts for geovisualization requirements.

SOLAP technology: Merging business intelligence with geospatial technology for interactive spatio-temporal exploration and analysis of data

On-line analytical processing (OLAP) has gained its popularity in database industry. With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and on-line analytical processing of spatial data. In this paper, we study methods for spatial OLAP, by integration of nonspatial on-line analytical processing (OLAP) methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies proposed, including approximation and partial materialization of the spatial objects resulted from spatial OLAP operations. Some techniques for selective materialization of the spatial computation results are worked out, and the performance study has demonstrated the effectiveness of these techniques.KeywordsData warehousedata miningon-line analytical processing (OLAP)spatial databasesspatial data analysisspatial OLAP

Data Warehouses (DW) and OnLine Analytical Processing (OLAP) tools are used in Business Intelligence (BI) applications in order to support decision-making processes. DWs are a specific type of database used to integrate, accumulate and analyze data from various sources. OLAP tools provide means to query and to analyze the warehoused information and produce online statistical summaries (indicators) at different levels of details. These indicators are computed using aggregate functions (e.g. Sum, Avg, Min, Max, etc.). Users can explore DWs by performing OLAP operations (e.g., Roll-up, Drill-down, etc.). Data mining algorithms can also be used in DWs, aiming towards improving data analysis; these techniques help to discern automatically the correlations and causal links between data. The increasing availability of geo-referenced data has made necessary the need to enrich OLAP with spatial analysis tools. OLAP systems were successfully adapted to solve problems in different areas of application and new kinds of decision support systems, named Spatial Data Warehouses (SDW) and Spatial OLAP (SOLAP) tools have been made available. SDWs are a collection of geographical information supporting spatial analysis. SOLAP tools allow users to perform spatio-temporal exploration of data: these tools combine OLAP analysis with cartographic visualization capabilities of GIS systems. The relevance of SOLAP technology Geoinformatica (2014) 18:269–272 DOI 10.1007/s10707-013-0196-9

Cloud computing systems handle large volumes of data by using almost unlimited computational resources, while spatial data warehouses (SDWs) are multidimensional databases that store huge volumes of both spatial data and conventional data. Cloud computing environments have been considered adequate to host voluminous databases, process analytical workloads and deliver database as a service, while spatial online analytical processing (spatial OLAP) queries issued over SDWs are intrinsically analytical. However, hosting a SDW in the cloud and processing spatial OLAP queries over such database impose novel obstacles. In this article, we introduce novel concepts as cloud SDW and spatial OLAP as a service, and afterwards detail the design of novel schemas for cloud SDW and spatial OLAP query processing over cloud SDW. Furthermore, we evaluate the performance to process spatial OLAP queries in cloud SDWs using our own query processor aided by a cloud spatial index. Moreover, we describe the cloud spatial bitmap index to improve the performance to process spatial OLAP queries in cloud SDWs, and assess it through an experimental evaluation. Results derived from our experiments revealed that such index was capable to reduce the query response time from 58.20 up to 98.89 %.

Query evaluation and optimization in deductive and object-oriented spatial databases

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.

Spatial Data Warehouses (SDWs) and Spatial On-Line Analytical Processing (SOLAP) systems are new technologies for the integration and the analysis of huge volume of data with spatial reference. Spatial vagueness is often neglected in these types of systems and the data and analysis results are considered reliable. In a previous work, the authors provided a new design method for SOLAP datacubes that allows the handling of vague spatial data analysis issues. The method consists of tailoring SOLAP datacubes schemas to end-users tolerance levels to identified potential risks of misinterpretation they encounter when exploiting datacubes containing vague spatial data. It this paper, the authors further their previous proposal by presenting different formal tools to support their method: it is an UML profile providing stereotypes needed to add vague, risks and tolerance levels information on datacubes schemas plus the formal definition of SOLAP datacubes schemas transformation process and functions.

Spatial Data Warehouses (SDW) and Spatial Online Analytical Processing (OLAP) systems are Business Intelligence technologies allowing efficient and interactive analysis of huge volume of geo-referenced data. Some efforts have been done to integrate complex spatial data such as field, generalized spatial data, etc., in such systems, at conceptual and physical levels. However, existing works do not support spatial networks (i.e. road networks), which define topological connections between spatial data. Thus, mandatory analysis capabilities offered by spatial networks cannot be introduced in the decision-making process powered by SOLAP systems. In this paper, motivated by relevance of using standards for the conceptual design of Data Warehouses and Geographic Information Systems, we propose a UML profile for data warehouses integrating spatial networks. We define our conceptual model in the Computer-Aided Software Engineering tool MagicDraw, and we propose a relational implementation in Oracle Spatial.

The main purpose of this paper is to introduce a new approach with a new data model and architecture that supports spatial and temporal data analytics for meteorological big data applications. The architecture is designed with the recent advances in the field of spatial data warehousing (SDW) and spatial and temporal big data analytics. Measured meteorological data is stored in a big database (NoSQL database) and analyzed using Hadoop big data environment. SDW provides a structured approach for manipulating, analyzing and visualizing the huge volume of data. Therefore, the main focus of our study is to design a Spatial OLAP-based system to visualize the results of big data analytics for daily measured meteorological data by using the characteristic features of Spatial Online Analytical Processing (SOLAP), SDW, and the big data environment (Apache Hadoop). In this study we use daily collected real meteorological data from various stations distributed over the regions. Thus, we enable to do spatial and temporal data analytics by employing spatial data-mining tasks including spatial classification and prediction, spatial association rule mining, and spatial cluster analysis. Furthermore, a fuzzy logic extension for data analytics is injected to the big data environment.

Agricultural energy consumption is an important environmental and social issue. Several diagnosis tools have been proposed to define indicators for analyzing the large-scale energy consumption of agricultural farm activities (year, farm, production activity, etc.). In Bimonte, Boulil, Chanet and Pradel (2012), the authors define (i) new appropriate indicators to analyze agricultural farm energy-use performance on a detailed scale and (ii) show how Spatial Data Warehouse (SDW) and Spatial OnLine Analytical Processing (SOLAP) GeoBusiness Intelligence (GeoBI) technologies can be used to represent, store, and analyze these indicators by simultaneously producing graphical and cartographic reports. These GeoBI technologies allow for the analysis of huge volumes of georeferenced data by providing aggregated numerical values visualized by means of interactive tabular, graphical, and cartographic displays. However, existing data collection systems based on sensors are not well adapted for agricultural data. In this paper, the authors show the global architecture of our GeoBI solution and highlight the data collection process based on agricultural ad hoc sensor networks, the associated transformation and cleaning operations performed by means of Spatial Extract Transform Load (ETL) tools, and a new implementation of the system using a web-services-based loosely coupled SOLAP architecture to provide interoperability and reusability of the complex multi-tier GeoBI architecture. Moreover, the authors detail how the energy-use diagnosis tool proposed in Bimonte, Boulil, Chanet and Pradel (2012) theoretically fits with the sensor data and the SOLAP approach.

This paper proposes a novel perspective of research on the challenging issue of modeling Spatial Data Warehouses (SDW) that nicely contributes to improve state-of-the-art proposals. This conveys in the so-called Spatial Data Warehouse Metamodel (SDWM) that allow us to enhance both coverage and expressive power of SDW modeling by means of the following amenities: (i) separating the conceptual SDW modeling from the conceptual (spatial) OLAP modeling; (ii) supporting the modeling of complex constructs in SDW; and (iii) stereotyping attributes and measures as spatial objects directly. All these contributions finally depict a novel perspective of research in the investigated scientific field, which breaks the actual trend of state-of-the-art initiatives, by pinpointing their limitations. We complete our analytical contribution by means of a real-life application implemented via SDWM, which highlights the benefits deriving from applying SDWM in contrast with traditional SDW modeling methodologies.