A data model for requirements analysis

Activity-centric data gather feedback on students' learning to enhance learning effectiveness. The heterogeneity and multigranularity of such data require existing data models to perform complex on-the-fly computation when responding to queries of specific granularity. This, in turn, results in latency. In addition, existing data models are inefficient in storing computed results, which are often required for follow-up analysis. These follow-up analyses depend largely on stakeholder objectives, which often impose constraints to the analysis process. In this article, we propose a context-based data model that addresses two challenges associated with learning analytics: the increased processing time due to multiple data granularities required from various stakeholder objectives, and the lack of support for archiving and updating of new information that exist during iterative analysis. We demonstrate how the proposed model can help support analysis and visualization via the XuetangX and ASSISTments datasets. Results show that although our proposed data model requires preprocessing, it requires less time to process queries associated with learning-activity attributes of various granularities compared to existing data models.

The Human Genome Project (HGP) plans to sequence the human genome by the beginning of the next century. It will generate DNA sequences of more than 10 billion bases and complex marker sequences (maps) of more than 100 million markers. All of these information will be stored in database management systems (DBMSs). However, existing data models do not have the abstraction mechanism for modelling sequences and existing DBMS`s do not have operations for complex sequences. This work addresses the problem of sequence modelling in the context of the HGP and the more general problem of an extensible object data model that can incorporate the sequence model as well as existing and future data constructs and operators. First, we proposed a general sequence model that is application and implementation independent. This model is used to capture the sequence information found in the HGP at the conceptual level. In addition, abstract and biological sequence operators are defined for manipulating the modelled sequences. Second, we combined many features of semantic and object oriented data models into an extensible framework, which we called the ``Extensible Object Model``, to address the need of a modelling framework for incorporating the sequence data model with other types of data constructs and operators. This framework is based on the conceptual separation between constructors and constraints. We then used this modelling framework to integrate the constructs for the conceptual sequence model. The Extensible Object Model is also defined with a graphical representation, which is useful as a tool for database designers. Finally, we defined a query language to support this model and implement the query processor to demonstrate the feasibility of the extensible framework and the usefulness of the conceptual sequence model.

최근 환경문제로 인해 보행, 자전거, 대중교통과 같은 친 환경 교통수단에 대한 관심이 높아지고 있다. 하지만 보행과 자전거는 경우에 따라 장거리 이동이 불가능하기 때문에 이용범위가 제한적이라는 단점이 있다. 반면에 대중교통은 교통체증이라는 사회적 문제점까지 해결할 수 있는 교통수단이다. 이러한 대중교통은 시간에 따라 운행여부가 달라진다는 특징을 갖고 있다. 하지만 현재 웹상에서 서비스중인 대중교통정보 안내 서비스는 이러한 특징을 반영하지 못하기 때문에 경우에 따라 사용자에게 잘못된 정보를 제공할 수 있다. 이러한 한계점을 해결하고 대중교통의 운행정보를 고려한 정보제공을 위해 본 연구에서는 STP(Space Time Point)데이터 모델을 제안하였다. STP데이터 모델은 객체의 최하위 개념을 포인트 단위로 인식하고 이들을 계층적으로 구성하여 객체를 표현할 수 있으며, 기존의 시공간 데이터 모델과는 달리 시간에 따라 객체정보가 변하는 다양한 동적 공간객체의 구현이 가능하다. 대전지역을 대상으로 대중교통 운행정보로 버스 및 지하철에 대한 STP모델을 설계 및 구축하고 이를 이용하여 동적 경로 안내 기능을 구현하였다. 구현한 경로안내 기능을 통해 대전국립현충원에서 한남대학교로 가는 경로를 시간대별로 탐색해 본 결과 대중교통의 운행에 제한이 발생하는 심야시간에는 운행이 제한되는 노선을 우회하는 경로를 안내함으로써 시간에 따라 서로 다른 경로를 나타내었다. 이는 기존 데이터 모델에서는 제공할 수 없는 결과로 실시간 운행정보를 고려한 경로안내가 가능함을 확인하였다. 이러한 기능은 실시간 교통정보나 통행이력정보등과 같은 다양한 데이터 모델의 적용을 통해 추가적인 기능 확장이 가능할 것으로 기대한다. Recent environmental concerns have made interest in environment-friendly transportation means such as walking, biking, and public transportation. However, since it is difficult to move long distance by walking or biking, their scope of application is rather limited. On the other hand, public transportation can solve traffic congestion, a recent social issue, though its usability may depend on its time schedule. Currently available information services on public transportation in the Web do not reflect well such traits of the public transportation; thus, in some cases, they may provide wrong information to end users. To solve such problems and provide information based on timetable of public transportations, this paper proposes a STP(Space Time Point) data model. Unlike existing space-time data models, this model recognizes the bottommost element of an object as a point and structures these points in hierarchical way to define an object. In particular, It can make it possible to implement a variety of dynamic spatial objects changing object information according to time. An objective of this study is to design a STP model for bus and subway based on timetables of public transportation in Daejeon area and builds a system to provide path navigation. With the designed navigation function, a path from the Daejeon National Cemetery to Hannam University was searched by time slot. The result showed that the system provided different paths by time, as the system guided different paths when bus operation was limited in midnight. As existing data model could not provide such results, it is confirmed that the system can provide path navigation based on real-time traffic information. It is expected that based on such functionality, it is possible to provide additional functionalities by applying diverse data models such as real-time transport information or traffic history information.

The niche concept has been recognised as a fundamental notion in biological and business systems, amongst others. Existing data models do not have adequate primitives that faithfully represent ecological niches for geographical information systems. This paper shows the limitations of the status quo, manifested in semantic heterogeneity and the meaningless integration of ecological data sets. We advocate an ontological engineering approach to ecological data modelling and propose two new data modelling primitives, Functional object granules [FOGs] and Functional Contexts, which can be used to represent niches. We present our data modelling environment, OntoCrucible, and illustrate our model and methodology in the context of a case study involving African smallholder farmer upliftment.KeywordsGeographical Information SystemSmallholder FarmerFunctional ContextOntological EngineeringSemantic HeterogeneityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

One of the goals in the design of database machines of the future is their generality. In addition to being capable of carrying out the common database management functions with high reliability and performance, some of these machines are intended to support more than one data model. A specific database machine, known as the DBC, is intended to support several existing data models. Although the DBC supports many data models, we single out the relational data model for this discussion. In particular, we have tried to concentrate mainly on the subject of database representation and query translation of System R-like database management systems. Some estimates of the storage requirements and performance gains are given in this paper. However, due to limited space, the detailed analysis is shown elsewhere in [22].

In the field of environmental management, inefficient piecemeal sectorial approaches are still widespread, particularly in water resources management, where concerns and decisions are usually split among a set of rather weakly linked stakeholders. Planning and management problems to be addressed are in most cases complex and need to be tackled in a global, integrated and holistic way, including from the different areas of science and knowledge from the various stakeholders. Therefore, the sharing and integration of data, knowledge and models has become a major issue. A lot of effort has been made in recent years to develop such integrative tools, including systems (IS), decision support systems (DSS) or integrated modelling frameworks (IMF). Although these tools achieved great progress in terms of linkages, there is currently no environment providing a full integration of the various kinds of data, models, and their visualisation. This is the general issue tackled by the present thesis, with developments based on a semantic, systemic formalism, and applications focusing on water management issues. The first development step taken was the creation of a systemic description of water management. Albeit the various aspects of water management are abundantly documented and well-known, an integrated description including interconnections was lacking. Therefore, a graphic representation of interconnected elements annotated with definitions was created. This innovative result constitutes an ontology (a formal representation of a knowledge domain) providing a robust reference and a fairly exhaustive reminder of the complexity of the water management field, including transdisciplinary aspects. In the second step, a data model was created, based on the new SYSMOD language. The latter proposes a new graphical language uniting the ontological and systemic approaches, with a strong emphasis on interactions. Whereas existing data models are generally specific to a field or designed for a target application, this new data model is generic and transdisciplinary. It can handle any kind of systemically organised data, which potentially includes any sort of environmental data. To visualise and manage the systems-based, generic data stored within the database (created on the basis of the generic data model) a new kind of visualisation was developed, proposing diagrams of interconnected nodes. Whereas existing systems are often data warehouses without a user-friendly graphical user interface, or geographic systems focussing on spatial data, this new diagram visualisation proposes views including spatial and non-spatial elements (such as lakes, power plants, stakeholders or laws) interconnected by interactions (such as fluxes or influences). Bound to the database, the tool was called an information on the system (ISS). It provides a way of graphically managing and integrating the systemically organised, generic, transdisciplinary data. Furthermore, the systems approach fosters the apprehension and communication of complex situations in a holistic way (involving many different elements, influences and interactions from various fields), through their representation into thematic views. In complement to the diagram visualisation, a reporting tool was developed to display the data linked to elements (numeric values, comments, files, etc.) in a web-browser fashion. To interoperate with external models, another module was developed on the top of the ISS. Finally, a fully-fledged general user interface was created to integrate all the previous developments into a single environment. To visualise the different facets of elements, a geographic and a chart view were added, in complement to the reporting view. This final tool was called the generic decision support (GenDSS), as its application to given situations can give rise to a series of views and linkages to models, thereby providing specific DSS functionalities for these situations. The successive developments were tested in Birmingham (United Kingdom). Test results indicated that the GenDSS is currently limited to managing a few hundred elements; beyond this quantity, the loading times and the user interface reactivity can become long and unresponsive. On the other hand, the application also demonstrated the strengths of the GenDSS which provides an innovative integrated solution to centralise transdisciplinary data in various formats (e.g. numeric values, texts, geometries, files), to holistically describe situations using complementary visualisations, and to test scenarios using externally-linked models. Thus, the GenDSS may prove an adequately versatile solution for large institutions or enterprises dealing with complex situations, heterogeneous and transdisciplinary data, and where requirements are continuously evolving, such as: a car manufacturing corporation (which would involve several different production and research sites in different countries with different regulations), a hospital, or a strategic environmental planning network (where the tool could help to break the transdisciplinary, sectorial barriers). In the case of water, the GenDSS could prove very useful to achieve a truly integrated water management approach.

An object-oriented data model for the finite element method is described in this paper. In the data model, which is based on the Entity-Relationship model, a finite element is treated as an object.The proposed data model is applied to the h-version adaptive mesh technique. In the h-version adaptive mesh technique, an element is divided into 2 or 4 elements recursively to reduce the error norm for the whole analysis domain. The present mesh refinement system is constructed using the C+ + object-oriented programming language for the implimentation of the data model. To verify the mesh refinement system, we have chosen some 2-dimensional stress concentration and singularity problems. The results show that the newly proposed data model is very useful for the h-version adaptive mesh technique. Moreover, the data model shows great promise for the integrated FEM systems.

Interoperability of e-government systems is suggested to increase transparency, efficiency, effectiveness, and customer service in the public sector. Generic data models are often seen as a way for achieving especially semantic interoperability. To assess how the contemporary data models support semantic e-government interoperability, we reviewed literature on data models suggested for the public sector in light of four features: standard modelling language, entity-relationship modelling, vocabulary for data exchange and methodology. The review contributes previous research by introducing a four-feature framework for assessing capability of e-government data models to enhance interoperability and by providing an up-to-date review of the generic data models for this purpose.

This research compares two approaches to instruction in entity-relationship data modeling. The findings indicate that the use of an intermediate modeling approach to assist subjects in organizing information to be modeled will improve performance as measured by the accuracy of the resulting data model.

On the basis of analyzing of the marine environment spatio-temporal data characteristics and the existing data model, proposes a conceptual model for marine environment data based on the feature and field to achieve integration of description and expression of feature, field, time, space and semantic domains. Designs and implements the service-oriented marine environment data organization and storage using Geography Markup Language (GML). Efficient data organization and management is crucial to marine environment visual services.

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.

A SERM based framework to optimize the modelling of entity life history

The Entity Relationship model is the de-facto standard for data modeling and has been in use for a long time already. This popularity also led to the development of various tools that support ER modeling. However, these tools are often inflexible, proprietary, constrained to specific platforms, and lack advanced features like (SQL) code generation. This paper introduces the bigER modeling tool. bigER offers various features for flexibly specifying and visualizing conceptual ER data models. Within the VS Code IDE, the tool enables hybrid and interactive modeling through a textual editor with a custom language to specify ER elements and an accompanying view to display and modify the graphical ER model. The bigER modeling tool is one of the first tools to incorporate the Language Server Protocol and to be distributed through the VS Code ecosystem. Due to its web technology-based architecture, it is platform-independent and easily extensible.

This research compares success in developing conceptual data models for the extended entity relationship (EER) model and Kroenke’s object oriented model. A laboratory study was used to evaluate model correctness for 38 subjects divided into two equally sized groups where each group was trained in one of the modeling methods. Modeling correctness is measured in terms of eight different facets of a conceptual data model: (1) entities/objects, (2) attribute/property identifiers, (3) categories, (4) unary one-one relationships, (5) binary one-many relationships, (6) binary many-many relationships, (7) ternary one-many-many relationships, and (8) ternary many-many-many relationships. The EER model provided significantly improved performance for the attribute/ property identifier, unary one-one relationship, and binary many-many relationship facets.

During data modelling, NoSQL databases are usually considered schema-less databases. They do not support rigid schema as traditional databases. Instead, offer flexibility and support dynamic schema, which means schema design can be changed at run time to support analytical tasks. NoSQL databases allow storing similar information with multiple schemas, which facilitates data evolution by eliminating restrictions on the schema. It raises the question of whether the schema matters in NoSQL databases, given that explicit schema declaration is not required before data storage. Furthermore, if the schema does matter, what is its potential impact on application performance? This paper examines these questions by taking three types of NoSQL databases: Document store, Column store, and Key-Value store. All possible schema structure alternatives are designed using a sample Entity Relationship (ER) model. Then, a set of common queries are performed on various schema alternatives to analyse the impact of query performance in terms of query response time, speedup factor, read and write Latency, and database size. The results suggest that the scheme alternatives affect the database performance in different NoSQL models.