Research Article: An object model with parametric polymorphism for dynamic segmentation

Dynamic segmentation is viewed as one of the most important functions of geographic information systems for transportation applications. Although the road network and associated events (e.g., pavement material, traffic volume, incidents) can be referenced to both space and time, the spatial and temporal dimensions have not been well integrated. Modeling space-varying, time-varying, and space-time-varying events in dynamic segmentation by using an object database approach that is in line with the Object Database Management Group standard is explored. A mechanism called parametric polymorphism is used to lift conventional data types to spatial, temporal, and spatiotemporal types for maintaining knowledge about events that could change spatially, temporally, and spatiotemporally along linear features. An associated object query language, DS-OQL, was designed to support the formulation of spatial, temporal, and spatiotemporal queries on the road and event information.

Pipeline database is the core of a Digital Pipeline system, and the key to build a pipeline database is to design a proper pipeline data model. In this chapter, the author introduces the concepts and development history of spatial data model, and explains the design ideas, frameworks, object models, and advantages of the third generation spatial data model—the Geodatabase. The spatial data engine ArcSDE of the Geodatabase and its working modes are also described. The typical pipeline data models in the pipeline industry are comparatively analyzed from many aspects. Based on the ArcGIS Pipeline Data Model (APDM), drawing on the design experience of present main pipeline data models, combined with the actual demands and circumstances of the author’s implementation of the Digital Pipeline projects, the author proposes a pipeline data model, named Pipeline Spatial Data Model, referred to as PSDM. The author elaborates on the design concepts, object models, object attributes, and modular design of PSDM, as well as, PSDM’s support for linear referencing and dynamic segmentation, pipeline real-time data, and the process and methods of PSDM implementation as a Geodatabase.

In Nigeria, literature on the integration of traffic of pavement condition and traffic characteristics in predicting road traffic accident frequency on 2-lane highways are scanty, hence this article to fill the gap. A comparison of road traffic accident frequency prediction models on IIesha-Akure-Owo road based on the data observed between 2012 and 2014 is presented. Negative Binomial (NB), Ordered Logistic (OL) and Zero Inflated Negative Binomial (ZINB) models were used to model the frequency of road traffic accident occurrence using road traffic accident data from the Federal Road Safety Commission (FRSC) and pavement conditions parameters from pavement evaluation unit of the Federal Ministry of Works, Kaduna. The explanatory variables were: annual average daily traffic (aadt), shoulder factor (sf), rut depth (rd), pavement condition index (pci), and international roughness index (iri). The explanatory variables that were statistically significant for the three models are aadt, sf and iri with the estimated coefficients having the expected signs. The number of road traffic accident on the road increases with the traffic volume and the international roughness index while it decreases with shoulder factor. The systematic variation explained by the models amounts to 87.7, 78.1 and 74.4% for NB, ZINB and OL respectively. The research findings suggest the accident prediction models that should be integrated into pavement rehabilitation.
 
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A database management system (DBMS) should provide capabilities for defining and enforcing certain integrity constraints that must hold for the data recorded in the database. These constraints are derived from the semantics of the data and from the rules of the system under study (SuS) that database represents. Every DBMS is based on a data model that provides the means to achieve data abstraction and to express database schema. Each data model has a specific set of integrity constraints types. Some of constraint types are common for several data models and some of them are typical for a data model. Beyond well-known constraint types there are more complex ones that are difficult to express and enforce within the data model. In this paper we deal with the extended tuple constraint (ETC) type. It is the extension of tuple constraint that is one of constraint types of relational data model. In this paper we explain the usage of extended tuple constraint in relational data model and its implementation in relational DBMSs. XML data model does not recognize ETC type. We specify the extended tuple constraint in XML data model and propose two techniques for implementation of ETC in XML DBMSs. Each of them depends on the characteristics of a selected XML DBMS. We use XQuery functions in eXist DBMS, and triggers in Sedna DBMS.

Most data stream management systems are based on extensions of the relational data model and query languages, but rigorous analyses of the problems and limitations of this approach, and how to overcome them, are still wanting. In this article, we elucidate the interaction between stream-oriented extensions of the relational model and continuous query language constructs, and show that the resulting expressive power problems are even more serious for data streams than for databases. In particular, we study the loss of expressive power caused by the loss of blocking query operators, and characterize nonblocking queries as monotonic functions on the database. Thus we introduce the notion of NB -completeness to assure that a query language is as suitable for continuous queries as it is for traditional database queries. We show that neither RA nor SQL are NB -complete on unordered sets of tuples, and the problem is even more serious when the data model is extended to support order—a sine-qua-non in data stream applications. The new limitations of SQL, compounded with well-known problems in applications such as sequence queries and data mining, motivate our proposal of extending the language with user-defined aggregates (UDAs). These can be natively coded in SQL, according to simple syntactic rules that set nonblocking aggregates apart from blocking ones. We first prove that SQL with UDAs is Turing complete. We then prove that SQL with monotonic UDAs and union operators can express all monotonic set functions computable by a Turing machine ( NB -completeness) and finally extend this result to queries on sequences ordered by their timestamps. The proposed approach supports data stream models that are more sophisticated than append-only relations, along with data mining queries, and other complex applications.

Relational data modeling can often be restrictive as it provides no direct facility for modeling polymorphic types, reified relations, multi-valued attributes, and other common high-level structures in data. This creates many challenges in data modeling and engineering tasks, and has led to the rise of more flexible NoSQL databases, such as graph and document databases. In the absence of structured schemas, however, we can neither express nor validate the intention of data models, making long-term maintenance of databases substantially more difficult. To resolve this dilemma, we argue that, parallel to the role of classical predicate logic for relational algebra, contemporary foundations of mathematics rooted in type theory can guide us in the development of powerful new high-level data models and query languages. To this end, we introduce a new polymorphic entity-relation-attribute (PERA) data model, grounded in type-theoretic principles and accessible through classical conceptual modeling, with a near-natural query language: TypeQL. We illustrate the syntax of TypeQL as well as its denotation in the PERA model, formalize our model as an algebraic theory with dependent types, and describe its stratified semantics.

Summary 1. Linear features associated with transportation and riparian corridors are known to inhibit the mobility of birds and other wildlife, yet the factors contributing to their barrier effects are poorly understood. The diversity of roads in urban landscapes provides an opportunity for elucidating the relative importance of factors such as noise, traffic volume, gap width and adjacent vegetation on animal movement. 2. Using an avian mobbing call as a lure, we tested the willingness of forest songbirds to cross four types of linear features in the urban landscape of Calgary, Alberta, Canada: (1) roads of varying widths and traffic volumes, (2) conventional railways and light transit lines, (3) transportation bridges across riparian corridors, and (4) rivers. 3. Using mixed effects logistic regression, we found that the size of the gap in vegetation was the most important determinant of movement (P < 0·001). As the gap in vegetation exceeded 30 m, the likelihood of movement decreased dramatically and by 45 m, birds were only half as likely to move across gaps as they were to move an equivalent distance in continuous tree cover. Traffic volume also had a significant dampening effect on movement (odds ratio = 0·952 per 1000 vehicle per day increase; P < 0·001) and generally explained more variation in the data than noise levels. 4. Railroads proved to be the most permeable of the features we tested, probably owing to their relatively narrow width, which never exceeded 30 m. Surprisingly, rivers were less permeable than the anthropogenic linear features we tested, with a significant barrier effect evident even at widths <50 m. 5. The birds in our study showed a marked preference for flying over, rather than under, transportation bridges, particularly when adjacent vegetation was available. 6. Synthesis and applications. Our results suggest that linear features, both anthropogenic and natural, can significantly impede the movements of forest songbirds and that managing adjacent vegetation is a potentially effective way to mitigate these barrier effects in cities and other fragmented landscapes.

Condition Based Maintenance Planning of low volume rural roads using GIS

The relational data model is the dominant paradigm in the commercial database market today, and it has been for several years. However, there have been challenges to the model over the years, and they have influenced its evolution and that of database technology. The object-oriented revolution that got started in programming languages arrived to the database area in the form of a brand new data model. The relational model managed not only to survive the newcomer but to continue becoming a dominant force, transformed into the object-relational model (also called extended relational, or universal) and relegating object-oriented databases to a niche product. Although this market has many nontechnical aspects, there are certainly important technical differences among the mentioned data models. In this article I describe the basic components of the relational, object-oriented, and object-relational data models. I do not, however, discuss query language, implementation, or system issues. A basic comparison is given and then future trends are discussed.

We propose relational data modeling as a tool for replacing the ad hoc and uncoordinated approaches commonly used throughout the social sciences to gather, store, and disseminate data. We demonstrate relational data modeling using global electoral and political institutional data. We define a relational data model as a map of concepts, their attributes, and the relationships between concepts developed using a formal language and according to a set of rules. To demonstrate the methodology, we design a simple relational data model of six concepts: countries, parties, elections, districts, institutions, and election results. Furthermore, we introduce a data model to solve the particularly vexing issue of party discontinuity (party splits, mergers, and alliances). We show how the solution facilitates computational tasks, such as the calculation of core measures of political phenomena (ex: electoral volatility). Ultimately, a relational data approach will play a central role in collective investments to develop advanced data capabilities, and thereby advance the accuracy, pace, and transparency of scholarship in the social sciences.

Part 1 Data modelling: a relational data model concepts of the relational data model, operations defined on a set of relations, normal operations defined on a set of instances, synthesis - data dictionary, example - production workshop, integrity rules integrity rule, introduction to dependencies, functional and dimensional dependencies, join and decomposition dependencies, inclusion dependencies, relation cycles, other integrity rules, table of ir ranges. Part 2 Transformations: introduction and access paths graph choice of an internal data model, accuracy of an internal structure, access paths graph, relation graph, join of embedded relations, plan of the second part, transformation of a decomposition into a relation graph, relation graph of a decomposition, use of hinges, from a relation graph to an internal data structure choice of an access paths graphs, computing methods of embedding access paths. Part 3 Decomposition of a relation: choosing a relation decomposition decomposing a relation, projection and extension of an integrity rule, qualities of a decomposition, choice crieria for a functional decomposition introduction, problems encountered, structural elements, a solution - mechanisms for completeness of keys and entities, basic decomposition introduction, keys to a relation, minimum fd equivalence classes, block of attributes and groups of fds, invariance of non-redundant bases, homogenous coverings and the basic covering, homogenous decompositions and the basic decomposition of a relation, completely homogenous decomposition, relation graph of a completely homogenous decomposition, FD appendix, Appendix: damping functional cycles damping a functional cycle, validation of a damping. Part 4 Perspectives: perspectives design process for databases, extended relational data model, control mechanisms, Appendix: use of this mechanism.

The National Center for Biotechnology Information (NCBI) has created a database collection that includes several protein and nucleic acid sequence databases, a biosequence-specific subset of MEDLINE, as well as value-added information such as links between similar sequences. Information in the NCBI database is modeled in Abstract Syntax Notation 1 (ASN.1) an Open Systems Interconnection protocol designed for the purpose of exchanging structured data between software applications rather than as a data model for database systems. While the NCBI database is distributed with an easy-to-use information retrieval system, ENTREZ, the ASN.1 data model currently lacks an ad hoc query language for general-purpose data access. For that reason, we have developed a software package, SORTEZ, that transforms the ASN.1 database (or other databases with nested data structures) to a relational data model and subsequently to a relational database management system (Sybase) where information can be accessed through the relational query language, SQL. Because the need to transform data from one data model and schema to another arises naturally in several important contexts, including efficient execution of specific applications, access to multiple databases and adaptation to database evolution this work also serves as a practical study of the issues involved in the various stages of database transformation. We show that transformation from the ASN.1 data model to a relational data model can be largely automated, but that schema transformation and data conversion require considerable domain expertise and would greatly benefit from additional support tools.

Series Introduction. Foreword: Michael F.Goodchild. Preface. Contrubutors. Down to the Sea in Ships: The Emergence of Marine GIS - Dawn Wright. Working on the Frontiers of Science: Applying GIS to the Coastal Zone - Darius J. Bartlett. Part One: Conceptual/ Technical Issues Data Models for Marine and Coastal Geographic Information Systems - Rongxing Li. An Algorithmic Approach to Marine GIS - Christopher Gold. Representations of Variability in Marine Environmental Data - Anne Lucas. Applying Spatio-Temporal Concepts to Correlative Data Analysis - Herman Varma. Linear Reference Data Models and Dynamic Segmentation: Application to Coastal and Marine Data - Andrew G. Sherin. Spatial Reasoning for Marine Geology and Geophysics - Dawn J. Wright. 2.5 and 3-D GIS for Coastal Geomorphology - Jonathan Raper Part Two: Applications Real-time GIS for Marine Applications - Gerald A. Hatcher Jr. and Norman Maher. Electronic Chart Disply and Information Systems (ECDIS): State-of-the-art in Nautical Charting - Robert Ward, Chris Roberts and Ron Furness. The National Oceanic and Atmospheric Administration's Vents Program GIS: Integration, Analysis and Distribution of Multidisciplinary Oceanographic Data - Christopher G. Fox and Andra M. Bobbitt. Integrated Geographical and Environmentally Remotely-Sensed Data on Marginal and Enclosed Basins - Vittorio Barale. Mapping Submarine Slope Failures - Brian G. McAdoo. Applications of GIS to Fisheries Management - Geoff J. Meaden. A User-Friendly Marine GIS for Multi-Dimensional Visualisation - Yafang Su. Active Techtonics: Data Acquisition and Analysis with Marine GIS - Chris Goldfinger Part Three: Institutional Issues Managing Marine and Coastal Data Sources: A National Oceonographic Data Centre Perspective on GIS - Bronwyn Cahill, Theresa Kennedy and Orla Ni. Significance of Coastal and Marine Geographical Information Systems within the Context of the United States National Geospatial Data Policies - Millington Locakwood and Cindy Fowler. GIS applications to Marine boundary Delimitations -Harold Palmer and Loin Pruett Information Quality Considerations for Coastal Data -Nancy Von Meyer, Kenneth E Foote and Donald J Huebner Epilogue - Darius J. Bartlett and Dawn J. Wright overview, brief history. Part 1: Conceptual/Technical Issue. Part 2: Applications. Part 3: Institutional Issues.

Computer applications in non-traditional areas have put requirements on conceptual data modeling. Some conceptual data models, being the tool of design databases, have been proposed. However, information in real-world applications is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data models and the design of databases with imprecision and uncertainty. In this chapter, a different level of fuzziness based on fuzzy set and possibility distribution theory will be introduced into the IFO data model and the corresponding graphical representations will be given. The IFO data model is then extended to a fuzzy IFO data model, denoted IF2O. In particular, we provide the approach to mapping an IF2O model to a fuzzy relational database schema.

The relational database model is the main focus of the database for the previous time. It provides robust data storage and structure that supports transaction characteristics and data retrieval with structured query language- SQL. The emergence of web technologies and clustering technology in large servers called for the need to store unstructured data and move away from the pattern of tables and static fields and not topic to transaction conditions, especially in distributed systems. Instead, it used CAP theory. New data models are emergence including: Document Data model, Key-Value model, Column-Family model, and Graph model, And new programming languages that deal with these models. An asset inventory system is a model of systems that are not topic to a fixed structure. Each location in the organization has fairly different assets and assets in the same location have different specifications that are difficult to organize in the form of tables and columns. For this reason using of NOSQL CouchDB database system was adopted and the use of MAP-REDUCE technology to organize data display and Cloudant Query in Mango-view method to retrieve data from the inventory database.