Existing Database Technology Research Articles

Hilbert Curve and Cassandra Based Indexing and Storing Approach for Large-Scale Spatiotemporal Data

Objectives Because of the fast growing acquisition of real-time spatiotemporal data for various applications such as smart city or real-time air-quality monitoring, the traditional database technologies can-not satisfy the higher standards for large-scale data indexing, querying, and storing operations. As the via-ble alternative, NoSQL databases that are scalable and possess fast input/output capabilities offer potential solutions to accommodate the needs. Methods We propose a Hilbert curve and Cassandra technologies based approach for efficient indexing and storing of large-scale spatiotemporal datasets aiming to provide an effective framework for processing, querying, and analyzing large amount of data with spatial and temporal features. For example, the dataset of vehicle trajectories contains valuable spatial and temporal features those are being employed in the real world. The collected spatiotemporal datasets are preprocessed in order to fit the proposed structures for different applications. Specifically, two types of query applications com -monly used in the real world are the spatiotemporal range query and query upon vehicle IDs respectively. Two corresponding indexing structures are designed and implemented in order to accommodate the requests. S2 Geometry Library open sourced by Google is utilized to divide the earth surface into grids, and data points fall in grids are assigned with the specific IDs as the keys. The keys and columns are so designed by applying the Hilbert curve and Cassandra techniques that the resultant structures will physically store the spatially neighboring data points close to each other, and they are more suitable for large-scale spatiotempo-ral data querying and analyzing applications. Results The datasets acquired from the real applications are used to conduct the computational experiments to validate the efficiency of the proposed approach. The que-ry efficiency and the time consumed to store large amount of spatiotemporal data are investigated and bench-marked against some existing database technologies. Conclusions The computational experiments reveal the superiority of the proposed approach comparing to the existing methodologies, the required time to store (insert) data in the database is reduced by 6 times while the time needed to query data is decreased by at least 10 times. The efficiency of the proposed methodology is validate further by applying it to query the vehicle trajectories gathering the real-time air quality data.

RETRACTED ARTICLE: Multimedia Imaging Model of Information System Based on Self-Organizing Capsule Neural Network and Game Theory

With the advent of multimedia technology, the application fields of computers are further broadened, such as computers widely used in video conferencing, video telephony, shopping mall shopping guides, tour guides, computer-aided integrated manufacturing systems, geographic information systems, computer-aided teaching, etc., with new The emergence of application fields and the increasing demands of people for complex data management are increasing, and the requirements for database management systems are increasing. In general, numbers and characters are formatted data, and existing database technologies, especially relational database technology, can be used for good access management. However, text, sound, and images are unformatted data, so traditional relational models cannot be described and processed. To this end, people began to research multimedia database technology in order to manage multimedia data. In this paper, we proposed a multimedia model of management system based on self-organizing capsule neural network and game theory. Experimental results show that the proposed method has higher efficiency and robustness.

Adaptive load forecasting using reinforcement learning with database technology

ABSTRACTLoad forecasting is an essential operation in the power utility industry. However, a common challenge is faced for adjusting forecasting models to fit the need for substations’ load prediction as well as minimizing expenditure in IT resources for repurposing these forecasting models to bigger datasets. The goal of this paper is to propose a novel solution that is responsive to these demands through the integration of reinforcement learning with load forecasting on existing database technology. To deal with the varying accuracy of the forecasting models on different substations’ loads, the proposed solution compares and uses the best models and recalibrate them iteratively by comparing the model’s prediction against the actual load data. As shown in empirical analysis, the solution interacts with the environment and performs the optimum forecasting routine intuitively.

Discovery of Hidden Relationship in a Large Data Itemsets through Apriori Algorithm of Association Analysis with UML

An association rule is a method to find out the frequent hidden relationship from a large amount of datasets in a database. Association analysis into existing database technology is very useful for indexing and query processing capabilities of database system and developing efficient and scalable mining algorithms as well as handling user specified or domain specific constraints and post processing the extracted patterns. In the present work, a methodology known as association analysis is presented which is very useful for discovery of interesting relationship hidden in large dataset, and an algorithm for generation of frequent data item set known as Apriori algorithm is used and validated the relations through Unified Modeling Language (UML). Authors used the lattice structure and also discussed the various association rules for the frequent data itemset which is found by Apriori algorithm. The different strategies in generation and traversal are breadth first and depth first search traversal. These techniques provide different tradeoff in terms of the input and output memory and computational time requirements. The entire concept is implemented by considering a real case study of Vehicle Insurance Policy system (VIPS) in context of Indian scenario.

Management and storage of in situ oceanographic data: An ECM-based approach

Management of scientific data poses a number of challenging problems involving both procedures and tools, and the field of oceanography makes no exception. During oceanographic cruises, significant amounts of heterogeneous data are collected by means of a wide variety of measuring devices, and the sequences of registered values mostly come as plain text files. Each instrument has its own data format. Raw data parsed from the acquisition files undergo some preliminary processing phases and, at the end, in the form of refined data, they can be considered ready to be kept as actual descriptions of the observed phenomena. This paper, based on experiences carried out in an oceanographic research center, describes problems and possible architectural solutions in managing heterogeneous oceanographic data from collection up to proper repositories. It is shown how a careful employment of ECM (Enterprise Content Management) systems may be beneficial in this setting, with no need to adopt complex ad hoc solutions that are difficult to maintain by personnel not specifically skilled in data-handling techniques. A data model to support the storage of refined data in structured repositories is developed as well. It is well-known that database technology is not pervasive yet in the scientific community, as it already is in other areas. The proper concerted use of ECM, DBMS, and possibly scientific workflow frameworks may represent the means to enable an effective adoption and a fruitful exploitation of the existing database technology also in the field of oceanography.

Where Do We Go From Here? Recording and Analysing Roman Coins from Archaeological Excavations

The publication of English Heritage's guidelines for the analysis and publication of coins from excavations has not met with acceptance by the relevant specialists. This paper takes the opportunity to look back over what we have been doing, consider what the guidelines suggest, and makes recommendations as to where we could be going. In particular it argues that we should be making more of existing database technologies and the internet, and that the analysis of coins should be integrated with other aspects of the archaeological record. The paper is not a new set of guidelines, but is intended to stimulate debate.

Building Intelligent Learning Database Systems

Induction and deduction are two opposite operations in data-mining applications. Induction extracts knowledge in the form of, say, rules or decision trees from existing data, and deduction applies induction results to interpret new data. An intelligent learning database (ILDB) system integrates machine-learning techniques with database and knowledge base technology. It starts with existing database technology and performs both induction and deduction. The integration of database technology, induction (from machine learning), and deduction (from knowledge-based sys-tems) plays a key role in the construction of ILDB systems, as does the design of efficient induction and deduction algorithms. This article presents a system structure for ILDB systems and discusses practical issues for ILDB applications, such as instance selection and structured induction.

Temporal data management

A wide range of database applications manage time-varying information. Existing database technology currently provides little support for managing such data. The research area of temporal databases has made important contributions in characterizing the semantics of such information and in providing expressive and efficient means to model, store, and query temporal data. This paper introduces the reader to temporal data management, surveys stale-of-the-art solutions to challenging aspects of temporal data management, and points to research directions.

Use of the World Wide Web for multisite data collection.

As access to the Internet becomes increasingly available, research applications in medicine will increase. This paper describes the use of the Internet, and, more specifically, the World Wide Web (WWW), as a channel of communication between EDs throughout the world and investigators who are interested in facilitating the collection of data from multiple sites. Data entered into user-friendly electronic surveys can be transmitted over the Internet to a database located at the site of the study, rendering geographic separation less of a barrier to the conduction of multisite studies. The electronic format of the data can enable real-time statistical processing while data are stored using existing database technologies. In theory, automated processing of variables within such a database enables early identification of data trends. Methods of ensuring validity, security, and compliance are discussed.

Building knowledge base management systems

Advanced applications in fields such as CAD, software engineering, real-time process control, corporate repositories and digital libraries require the construction, efficient access and management of large, shared knowledge bases. Such knowledge bases cannot be built using existing tools such as expert system shells, because these do not scale up, nor can they be built in terms of existing database technology, because such technology does not support the rich representational structure and inference mechanisms required for knowledge-based systems. This paper proposes a generic architecture for a knowledge base management system intended for such applications. The architecture assumes an object-oriented knowledge representation language with an assertional sublanguage used to express constraints and rules. It also provides for general-purpose deductive inference and special-purpose temporal reasoning. Results reported in the paper address several knowledge base management issues. For storage management, a new method is proposed for generating a logical schema for a given knowledge base. Query processing algorithms are offered for semantic and physical query optimization, along with an enhanced cost model for query cost estimation. On concurrency control, the paper describes a novel concurrency control policy which takes advantage of knowledge base structure and is shown to outperform two-phase locking for highly structured knowledge bases and update-intensive transactions. Finally, algorithms for compilation and efficient processing of constraints and rules during knowledge base operations are described. The paper describes original results, including novel data structures and algorithms, as well as preliminary performance evaluation data. Based on these results, we conclude that knowledge base management systems which can accommodate large knowledge bases are feasible.

A system for identifying datasets for GIS users

Abstract The collection and entry of data are very expensive, and have been identified as a major cost in establishing a working GIS. There are, therefore, considerable benefits to be gained from establishing a mechanism that will identify the existence of datasets that have already been collected and entered and may therefore be available for use by others. With the widespread use of GIS and the consequent very rapid increase in the number of datasets being created, the problem is unlikely to be solved by using existing database technology to hold information about available datasets, and requires a radically-different approach. This article describes a system being developed by the Midlands Regional Research Laboratory which aims to solve the problem of identifying relevant existing datasets. Both holders of data and enquirers will provide a description of the data in their own words, which will be refined interactively by the system to remove ambiguities before conducting the search. The system is to be used for the identification and retrieval of metadata within the ESRC-funded Global Environmental Change Data Network Facility in the United Kingdom.