Geometric Querying of Time-Dependent Data for Data Mining in Molecular Dynamics

The following dissertation presents a new vision of Data Warehouses (DW) evolution – a new generation of DW models called Advanced Data Warehouses (AdvDW). The ability to formulate new models of classical data warehouse for new global challenges is strongly limited. Moreover, the research on spatio-temporal data warehouses (STDW) is rather insufficient. The new vision of DW evolution presented in the following work creates a next level of research promoting AdvDW models as a self-reliant approach being much more than only features derived from generalized nature of classical DWs. As an all-out solutions for problems, framework concepts, models, methods, prototypes, effective characteristics and their analyses were defined: - Highly Hierarchical Aggregation and E-Receptors Systems (2HAa*). - Aggregates Granary including MultiRing Data Warehouse (MultiRingDW). - Spatio - temporal data warehouses (D)STDW as the class I of AdvDW. - Strongly preserved data warehouses (SPrevDW) as the class II of AdvDW. - Grid data warehouses (GDWSA) as the class III of AdvDW. - Stream data warehouses StrDW) as the class IV of AdvDW. The realization of above-mentioned problem groups was verified with prototype software. A number of effectiveness characteristics of AdvDW was presented along with their evaluation.

The increasing demand for the use of database systems as an integrating factor in CAD/CAM applications has necessitated the development of database systems with appropriate modelling and retrieval capabilities. One essential problem is the treatment of geometric data which has led to the development of spatial databases. Unfortunately, most proposals only deal with simple geometric objects like multidimensional points and rectangles. On the other hand, there has been a rapid development in the field of representing geometric objects with free form curves or surfaces, initiated by engineering applications such as mechanical engineering, aviation or astronautics. Therefore, we propose a concept for the realization of spatial retrieval operations on geometric objects with free form boundaries, such as B-spline or Bezier curves, which can easily be integrated in a database management system. The key concept is the encapsulation of geometric operations in a so-called query processor. First, this enables the definition of an interface allowing the integration into the data model and the definition of the query language of a database system for complex objects. Second, the approach allows the use of an arbitrary representation of the geometric objects. After a short description of the query processor, we propose some representations for free form objects determined by B-spline or Bezier curves. The goal of efficient query processing in a database environment is achieved using a combination of decomposition techniques and spatial access methods. Finally, we present some experimental results indicating that the performance of decomposition techniques is clearly superior to traditional query processing strategies for geometric objects with free form boundaries.

Data warehouses are information repositories specialized in supporting decision making. Since the decisional process typically requires an analysis of historical trends, time and its management acquire a huge importance. In this paper we consider the variety of issues, often grouped under term temporal data warehousing, implied by the need for accurately describing how information changes over time in data warehousing systems. We recognize that, with reference to a three-levels architecture, these issues can be classified into some topics, namely: handling data/schema changes in the data warehouse, handling data/schema changes in the data mart, querying temporal data, and designing temporal data warehouses. After introducing the main concepts and terminology of temporal databases, we separately survey these topics. Finally, we discuss the open research issues also in connection with their implementation on commercial tools.

While data warehousing (DW) has emerged as a key component of many organizations' information systems, few studies have assessed companies' DW practices. This research examines a range of DW development and management issues. Data collected from more than two dozen large companies suggest that the DW concept has been implemented quite differently across enterprises, and that DW practices are still at an early stage of development. The results are also compared across two different DW architecture types, the hub & spoke and federated data mart approaches. This analysis suggests that the choice of architecture appears to have an important effect on a number of DW development and management measures. The results of this study should be useful to companies looking to initiate or expand their DW operations and to researchers in understanding the current scope and operations of companies' data warehousing efforts.

The availability of low cost sensors has led to an unprecedented growth in the volume of spatial data. Unfortunately, the time required to evaluate even simple spatial queries over large data sets greatly hampers our ability to interactively explore these data sets and extract actionable insights. While Graphics Processing Units~(GPUs) are increasingly being used to speed up spatial queries, existing solutions have two important drawbacks: they are often tightly coupled to the specific query types they target, making it hard to adapt them for other queries; and since their design is based on CPU-based approaches, it can be difficult to effectively utilize all the benefits provided by the GPU. As a first step towards making GPU spatial query processing mainstream, we propose a new model that represents spatial data as geometric objects and define an algebra consisting of GPU-friendly composable operators that operate over these objects. We demonstrate the expressiveness of the proposed algebra and present a proof-of-concept prototype that supports a subset of the operators, which shows that it is orders of magnitude faster than a CPU-based implementation and outperforms custom GPU-based approaches.

Libraries are service units with high storage complexity as evidenced by more data being stored for each year. The data that is not integrated makes the complex problem because every year the process that is carried out continues to increase, especially for the circulation of loans. As the number of books increases, the circulation of borrowing increases every year. On the other hand, the library must know exactly what collection of books they have and the transactions it has made. A lot of data is owned by the library cannot be utilized optimally, so that the managerial is unable to make full use of the data. In University scale libraries, this problem increases when the data is not fully integrated. In this study, the implementation of a star schema was carried out to solve problems related to data integration using a nine-step methodology, which includes selection, item selection, process dimensions, fact selection, fact storage, ensuring dimension tables, selecting database duration, changing dimensions, determining priorities, and query models. The results of this study indicate that the star schema can be implemented in the case of libraries, data warehouses and OLAP to support decision making for adding books, and produced 3 dimensions of the 4 grains found.

One of the important research and technological issues in data warehouse performance is the optimization of analytical queries. Most of the research have been focusing on optimizing such queries by means of materialized views, data and index partitioning, as well as various index structures including: join indexes, bitmap join indexes, multidimensional indexes or index-based multidimensional clusters. These structures neither well support navigation along dimension hierarchies nor optimize joins with the Time dimension, which in practice is used in the majority of analytical queries. In this chapter we overview the basic index structures, namely: a bitmap index, a join index, and a bitmap join index. Based on these indexes, we show how to build another index, called Time-HOBI, for optimizing queries that address the Time dimension and compute aggregates along dimension hierarchies. We further discuss the extension of the index with additional data structure for storing aggregate values along the hierarchical structure of the index. The aggregates are used for speeding up aggregate queries along dimension hierarchies. Furthermore, we show how the index is used for answering queries in an example data warehouse. Finally, we discuss its performance-related characteristics, based on experiments.

Temporal data warehouses (TDWs) have been developed for the management of time-varying data in dimensions. This paper presents a new approach for the logical modelling of TDWs. The novel design is based on the integration of two schemata, the star schema and the snowflake schema, to the temporal starnest schema. Time in the temporal starnest schema is not treated as another dimension but as time attributes in every temporal dimension, i.e., dimension tables dependent on time. Time manipulation functions for the treatment of time attributes are provided in the query language. The temporal starnest schema proposed in this paper expresses naturally hierarchy levels by the clustering of data in nested tables, with resulting description of aggregation levels for a dimension in a natural way. The proposed extension is applied to a TDW for accident monitoring in crossroads where the expressive power of the model is exemplified with several temporal queries expressed in a suitable extension of SQL standard.

Chapter 63 - Processing Star Queries on Hierarchically-Clustered Fact Tables

Geometric algorithms are based on geometric objects such as points, lines and circles. The term kernel refers to a collection of representations for constant-size geometric objects and operations on these representations. This paper describes how such a geometry kernel can be designed and implemented in C++, having special emphasis on adaptability, extensibility and efficiency. We achieve these goals following the generic programming paradigm and using templates as our tools. These ideas are realized and tested in Cgal, the Computational Geometry Algorithms Library, see http://www.cgal.org/.

Geometric algorithms are based on geometric objects such as points, lines and circles. The term Kernel refers to a collection of representations for constant-size geometric objects and operations on these representations. This paper describes how such a geometry kernel can be designed and implemented in C++, having special emphasis on adaptability, extensibility and efficiency. We achieve these goals following the generic programming paradigm and using templates as our tools. These ideas are realized and tested in CGAL [9], the Computational Geometry Algorithms Library.KeywordsComputational geometrylibrary designgeneric programming

The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

Intelligent Service Platform of Manufacturing Process and Tool Based on Data Warehouse

With the growth of cloud computing, data warehouse can benefit from this new technology. Cloud storage holds the advantages of high reliability, simple management and cost-effective. However, as each technological advance, the cloud computing also brings its own risks, data security issues are one of the top concerns when dealing with sensitive data. To ensure the confidentiality of sensitive data, the owner must encrypt it before outsourcing. When data are encrypted, a tradeo □ must be achieved between security and performance. Existing solutions are not suitable for cloud data warehouse because those solutions induce a heavy overhead in terms of data storage and query performance. So because the nature of data warehouse, a suitable solution must be proposed that balances the security and the performance of data warehouse hosted in the cloud. In this paper, we propose (CloudWar) an efficient schema based on a homomorphic encryption algorithm for securing and querying a data warehouse hosted in the cloud. CloudWar addresses the short coming of existing approaches by reducing the volume overhead and time complexity of query processing will still maintaining good privacy and performance. Also, this new schema proposes a new signature for verifying the integrity of data when received from the cloud. Theoretical and experimental results show the efficiency of CloudWar in terms of privacy and performance with respect to other solutions.

Digital data have become a torrent engulfing every area of business, science and engineering disciplines. In the age of Big Data, deriving values and insights from large amounts of data using rich analytics becomes an important differentiating capability for competitiveness, success and leadership in every field. Scientists and engineers of many different domains are increasingly clamouring for mechanisms to manage and analyse the massive quantities of information now available in order to obtain new answers and extract from it maximum value. Computational modelling and simulation is the central technology to numerous of these domains. Molecular Dynamics (MD) is a computational simulation technique that describes the physical forces and movements of interacting microscopic elements such atoms and molecules. MD has important applications in the fields of chemistry, biotechnology, pharmaceutical industry, energy, climate or materials science, among others. Advanced MD algorithms include not only Molecular Mechanics (MM), but also Quantum Mechanics (QM) approaches, raising important big data challenges still to be sorted out. MD simulations perform an iterative process generating large amounts of data in streaming. Current software technology is far from being able to manage, analyze and visualize the extremely large and complex data sets generated by important molecular processes. This paper analyzes the current big data limits in the Computational Chemistry field, especially in the MD processes. To overcome these challenging situations, this work provide guidance for future research including advances in scalable algorithms for data analysis, dynamic query technology, data models and storage strategies, parallel executions, I/O optimization, and interactive visual exploration and analysis of MD data.