Schema Design Research Articles

Cadastral Systems Re-engineering in Urban Zimbabwe

Land is undoubtedly the most important resource in any country as it contributes to economic development. The cadastre is a component of the land administration system (LAS) that is crucial for managing land. It is thus of importance for a nation to have both a functional cadastral system for determining parcel boundaries and a functional cadastral information system, for managing the land parcels. The cadastral survey system and cadastre in Zimbabwe are largely manual with even the lodgement of completed surveys for examination and approval still analogue. This has an impact on the time it takes to complete a land transaction. Land can ideally drive the economy yet part of the value is lost due to lengthy land transaction procedures. The land administration system is supposed to consist of value adding processes in which several actors interact in a workflow which creates new or transfers parcels. This workflow is associated with transaction costs, part of which can be employed for maintenance of the cadastral information system. This article presents business processes for the land development process in Zimbabwe but focusing on City of Gweru (CoG) and City of Mutare (CoM) municipalities as the major organisations under study. Municipalities in Zimbabwe are governed by the Regional Town and Country Planning Act, so such a single process and workflow model can fit into the cadastral information system of all municipalities in Zimbabwe with insignificant changes. The broader scope of this study is towards the design of a conceptual schema for automating the land administration system and particularly, the cadastre component, for municipalities in Zimbabwe. This study presents the workflows for the current land development system with CoG and CoM as the current focal points. Information on current automation efforts or reforms by other major municipalities to include Bulawayo, Kadoma and Kariba is also presented. The overall theme of this paper is to discuss cadastral reform through automation of cadastral processes in municipalities.

The combination of adaptive database SDM and multi-output SVM for eddy current testing

Purpose – Eddy current testing (ECT) is a nondestructive testing method for the detection of flaws that uses electromagnetic induction to find defects in conductive materials. In this method, eddy currents are generated in a conductive material by a changing magnetic field. A defect is detected when there is a disruption in the flow of the eddy current. The purpose of this paper is to develop a new noniterative inversion methodology for detecting degradation (defect characterization) such as cracking, corrosion and erosion from the measurement of the impedance variations. Design/methodology/approach – The methodology is based on multi-output support vector machines (SVM) combined with the adaptive database schema design method (SDM). The forward problem was solved numerically using finite element method (FEM), with its accuracy experimentally verified. The multi-output SVM is a statistical learning method that has good generalization capability and learning performance. FEM is used to create the adaptive database required to train the multi-output SVM and the genetic algorithm is used to tune the parameters of multi-output SVM model. Findings – The results show the applicability of multi-output SVM to solve eddy current inverse problems instead of using traditional iterative inversion methods which can be very time-consuming. With the experimental results the authors demonstrate the accuracy which can be provided by the multi-output SVM technique. Practical implications – The work allows extending the capability of the experimentation ECT defect characterization system developed at LGEP. Originality/value – A new inversion method is developed and applied to ECT defect characterization. This new concept introduces multi-output SVM in the context of ECT. The real data together with estimated one obtained by multi-output SVM model are compared in order to evaluate the effectiveness of the developed technique.

Special Issue on Creating Community-Based Robust and Resilient Society

Special Issue on Creating Community-Based Robust and Resilient Society

Empirical evidence for the usefulness of Armstrong tables in the acquisition of semantically meaningful SQL constraints

SQL schema designs result from methodologies such as UML, Entity-Relationship models, description logics, or relational normalization. Independently of the methodology, sample data is promoted by academia and industry to consolidate the schema designs produced. SQL constraints are an abstract standard-compliant encoding of the designers' perception about the semantics of an application domain. Armstrong tables can visualize SQL constraints concisely, in the sense that they satisfy all constraints perceived meaningful and violate all constraints perceived meaningless. Using new empirical measures we investigate how Armstrong tables help design teams recognize domain semantics. Extensive experiments confirm that users of Armstrong tables are likely to recognize domain semantics they would overlook otherwise. Armstrong tables therefore complement existing schema design methodologies in producing quality schemata that process data efficiently.

Exploiting semantics for XML keyword search

XML keyword search has attracted a lot of interests with typical search based on lowest common ancestor (LCA). However, in this paper, we show several problems of the LCA-based approaches, including meaningless answers, incomplete answers, duplicated answers, missing answers, and schema-dependent answers. To handle these problems, we exploit the semantics of object, object identifier, relationship, and attribute (referred to as the ORA-semantics). Based on the ORA-semantics, we introduce new ways of labeling and matching. More importantly, we propose a new semantics, called CR (Common Relative) for XML keyword search, which can return answers independent from schema designs. To find answers based on the CR semantics, we discover properties of common relative and propose an efficient algorithms. Experimental results show the seriousness of the problems of the LCA-based approaches. They also show that the CR semantics possesses the properties of completeness, soundness and independence while the response time of our approach is faster than the LCA-based approaches thanks to our techniques.

Research in Packaging Design Schema and its Application Model

Research in Packaging Design Schema and its Application Model

Spatial data warehouses and spatial OLAP come towards the cloud: design and performance

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 %.

The design of valid multidimensional star schemas assisted by repair solutions

Multidimensional schemas are used to model data warehouse systems, a special type of large databases dedicated for decision support. Several approaches have been proposed in this domain to ensure the satisfaction of decision makers needs and the accuracy of the generated schemas. This paper presents an approach for the validation of multidimensional star schema assisted by repair solutions. Our approach aims to assist designers by detecting constraint violations and proposing repair solutions based on a number of error-based rules formalized in Prolog. Its efficiency stems from using both linguistic similarity computation and a set of heuristics developed by bottom-up design methods to produce warnings about semantic constraint violations and their impact on the quality of the analysis results.

Divide & conquer-based inclusion dependency discovery

The discovery of all inclusion dependencies (INDs) in a dataset is an important part of any data profiling effort. Apart from the detection of foreign key relationships, INDs can help to perform data integration, query optimization, integrity checking, or schema (re-)design. However, the detection of INDs gets harder as datasets become larger in terms of number of tuples as well as attributes. To this end, we propose Binder, an IND detection system that is capable of detecting both unary and n-ary INDs. It is based on a divide & conquer approach, which allows to handle very large datasets -- an important property on the face of the ever increasing size of today's data. In contrast to most related works, we do not rely on existing database functionality nor assume that inspected datasets fit into main memory. This renders Binder an efficient and scalable competitor. Our exhaustive experimental evaluation shows the high superiority of Binder over the state-of-the-art in both unary (Spider) and n-ary (Mind) IND discovery. Binder is up to 26x faster than Spider and more than 2500x faster than Mind.

TPτ Model Transformation: A Systematic Modelling Framework to Handle Internal Time Delays in Control Systems

AbstractVarying time delays are inherent and unavoidable properties of a large set of control systems causing stability issues in most cases. Control design methods that guarantee the stability, such as those based on the Lyapunov–Krasovskii functional, are mathematically highly complicated and therefore have hardly spread into everyday engineering practice. This paper introduces a systematic modelling and control design schema enabling a gateway between the different delayed system representations and the tensor product (TP) type convex polytopic models which allows the direct use of linear matrix inequality (LMI)‐based controller and observer synthesis. The approach presented assumes known, bounded, time‐varying internal time‐delays where the delay derivative is not considered. The proposed modelling scheme is demonstrated via a numerical example.

Implementation of database schema design in distributed environment

Implementation of database schema design in distributed environment

Adaptive Database Schema Design for Multi-Tenant Data Management

Multi-tenant data management is a major application of Software as a Service (SaaS). For example, many companies want to outsource their data to a third party that hosts a multi-tenant database system to provide data management services. The multi-tenant database system needs to have high performance, low space requirement, and excellent scalability. One big challenge is devising a high-quality database schema. Independent Tables Shared Instances (ITSI) and Shared Tables Shared instances (STSI) are two state-of-the-art approaches to designing the schema. However, they suffer from some limitations. ITSI has poor scalability since it needs to maintain large numbers of tables. STSI achieves good scalability at the expense of poor performance and high space overhead. Thus, an effective schema design method that addresses these problems is needed. In this paper, we propose an adaptive database schema design method for multi-tenant applications. We trade-off ITSI and STSI and find a balance between them to achieve good scalability and high performance with low space requirement. To this end, we identify the important attributes and use them to generate an appropriate number of base tables. For the remaining attributes, we construct supplementary tables. We discuss how to use the kernel matrix to determine the number of the base tables, apply graph-partitioning algorithms to construct the base tables, and evaluate the importance of attributes using the well-known PageRank algorithm. We propose a cost-based model to adaptively generate the base tables and supplementary tables. Our method has the following advantages. First, our method achieves high scalability. Second, our method achieves high performance and can trade-off the performance and space requirement. Third, our method can be easily applied to existing databases (e.g., MySQL) with minor revisions. Fourth, our method can adapt to any schemas and query workloads including both OLAP and OLTP applications. Experimental results on both real and synthetic datasets show that our method achieves high performance and good scalability with low space requirement and outperforms state-of-the-art methods.

Web-based Implementation for Marine Casualty Information on Google Map

Safety navigation of vessels is always the most important issue in maritime field. Recently, according to increasing the use of electronic equipment onboard, the requests for safety related information services have been growing up. To support this, International Hydrographic Organization suggests the concept of common maritime data structure. Since 2010, S-100 standard and relative product specifications have been defined as a part of that concept, covering the electronic navigational chart data and safe navigation related data. In this paper, we introduce an empirical application of S-100 standard, applied to marine casualty information. We analyze marine casualty cases reported in Korea for recent four years, make XML documents with schema design and then implement an application to present the marine casualty information on google map.

SU-E-T-544: A Radiation Oncology-Specific Multi-Institutional Federated Database: Initial Implementation

Purpose: To implement a common database structure and user-friendly web-browser based data collection tools across several medical institutions to better support evidence-based clinical decision making and comparative effectiveness research through shared outcomes data. Methods: A consortium of four academic medical centers agreed to implement a federated database, known as Oncospace. Initial implementation has addressed issues of differences between institutions in workflow and types and breadth of structured information captured. This requires coordination of data collection from departmental oncology information systems (OIS), treatment planning systems, and hospital electronic medical records in order to include as much as possible the multi-disciplinary clinical data associated with a patients care. Results: The original database schema was well-designed and required only minor changes to meet institution-specific data requirements. Mobile browser interfaces for data entry and review for both the OIS and the Oncospace database were tailored for the workflow of individual institutions. Federation of database queries--the ultimate goal of the project--was tested using artificial patient data. The tests serve as proof-of-principle that the system as a whole--from data collection and entry to providing responses to research queries of the federated database--was viable. The resolution of inter-institutional use of patient data for research is still not completed. Conclusions: The migration from unstructured data mainly in the form of notes and documents to searchable, structured data is difficult. Making the transition requires cooperation of many groups within the department and can be greatly facilitated by using the structured data to improve clinical processes and workflow. The original database schema design is critical to providing enough flexibility for multi-institutional use to improve each institution s ability to study outcomes, determine best practices, and support research. The project has demonstrated the feasibility of deploying a federated database environment for research purposes to multiple institutions.

Lessons Learned Developing the Response Action Record for the Deepwater Horizon Endangered Species Act Biological Assessment

ABSTRACT In an oil spill response environment, urgency looms, and virtually every action is geared toward immediate needs. Clean-up, safety, and listed species protection are at the forefront of the collaborative efforts carried out by an incident management team. However, these needs do not complete the obligation of the Federal Action Agency responsible for the event. This agency must also complete an Endangered Species Act (ESA) Biological Assessment (BA). To do that for a spill of national significance, it is paramount that response personnel track certain details about their daily operations. Unfortunately, in the BA for the Deepwater Incident Response, the action record had to be reconstructed forensically. Although operational permits to work, otherwise known as Shoreline Treatment Recommendations, used standard geographic references and response action terms, they are merely prescriptions for activity and provide only maximum default assumptions. To gain vital insight into more specific temporal elements such as frequency, intensity, and duration, daily response reports were required. These reports were not gathered into a central geodatabase along the way. They were printed to paper, boxed, shipped to a documentation unit, and scanned into image files. These files were organized into approximately 30,000 document sets of up to 4,000 pages each. Qualitative document content analysis was used to distill the needed details from these image sets into a database. This technique for generating the needed data for an effects analysis is arduous. However, the process of its development has produced valuable lessons learned. Here we present the needed schema design and architecture to promote a seamless transition on future responses from the urgency of immediate need to inevitability of post-spill ESA obligation.

Design of Data Management System for Remote ECG Monitoring

This paper presents a design schema about remote electrocardiograph (ECG) monitoring system. There are three parts in this system which include hardware detection system based on MSP430, Zigbee wireless sensor network and data management software system on PC. This paper mainly provides information for study and design of the data management software system using visual C++ program to realize immediate communication of medical monitoring. The data management software system will achieve many functions such as serial communication, real-time dynamic display of 12-lead ECG waveform and physiological parameter which include Heart Rate (HR).

빙해수조 모형빙에 관한 네트워크형 데이터베이스 구축

In the KIOST MOERI model ice basin, a lot of experiments to find the material properties of model ice and model ship tests are being performed. And therefore many data information was produced, yet it was saved as excel data format. In order to manage and use the data more effectively and systematically, a proper database management is required. To make realistic engineering DBMS(database management system), understanding of test process, the usage of data, design of data schema and computer language and system management are required. In this paper, basic design process and usage of DBMS based on network and multiple user concept is introduced.

Autonomic physical database design – from indexing to multidimensional clustering

Abstract Database design is a well-studied and well-understood problem in developing database applications. However, modern application requirements in terms of query response time, query throughput, database sizes, database maintenance and database interactivity capabilities on the one hand, and the manifold features of modern DBMS for physical schema design such as numerous different index structures, materialized views, partitioning, and clustering schemes on the other hand make high demands on the database design process. With this growing number of impact factors on physical database design automatic solutions have been developed and are provided in most systems, while research in this area still continues. In this paper, we give a survey on the state of the art in autonomic physical design. Starting with the classic index tuning problem and possible solutions, we describe further design problems such as choosing materializations of aggregations for OLAP and multidimensional clustering schemes. Finally, we point out to most recent challenges resulting from a shift in requirements and give a direction on how to tackle these.

Functional Dependency based on XML Node Sets

As an essential basis of relational database theory, integrity constraints such as functional dependency provide a basis for well-designed databases. Integrity constraints are also useful for the normalization of the XML schema design in the expensive applications of XML data. As a semi- structure feature, XML data are usually located by a path expression and multiple data items may be represented by the same path. Thus, functional dependencies for XML should be constraints between sets of XML data items if the path expression is used. These constraints also result in data redundancy. Same as functional dependency, this kind of data redundancy for XML can lead to update anomalies too. This paper proposes a kind of XML integrity constraint to describe the dependent relationship between different sets of XML data items, and defines a general functional depend- ency based on XML node sets. Moreover, this paper propos- es a group of inference rules for the implication problem of the XML functional dependency, and proves that they are sound and complete. Index Terms—Integrity constraints, XML, Functional de- pendencies, Database semantics

Metadata in Archival and Cultural Heritage Settings: A Review of the Literature

This article provides an overview of the literature related to metadata in the context of archives and museums. This article has several goals: to outline current practices, theories, and models; to discuss existing metadata schemas; and to identify theoretical models that apply to metadata schema design in this context. The article also explores best practices and identifies gaps in the literature; it offers suggestions for future research on metadata in archives and cultural heritage institutions.