Realm-based spatial data types: The ROSE algebra

Spatial data types or algebras for database systems should (1) be fully general, that is, closed under set operations, (2) have formally defined semantics, (3) be defined in terms of finite representations available in computers, (4) offer facilities to enforce geometric consistency of related spatial objects, and (5) be independent of a particular DBMS data model, but cooperate with any. We present an algebra that usesrealms as geometric domains underlying spatial data types. A realm, as a general database concept, is a finite, dynamic, user-defined structure underlying one or more system data types. Problems of numerical robustness and topological correctness are solved within and below the realm layer so that spatial algebras defined above a realm have very nice algebraic properties. Realms also interact with a DMBS to enforce geometric consistency on object creation or update. The ROSE algebra is defined on top of realms and offers general types to represent point, line, and region features, together with a comprehensive set of operations. It is described within a polymorphic type system and interacts with a DMBS data model and query language through an abstractobject model interface. An example integration of ROSE into the object-oriented data model O₂ and its query language is presented.     

Abstract-driven pattern discovery in databases

The problem of discovering interesting patterns in large volumes of data is studied. Patterns can be expressed not only in terms of the database schema but also in user-defined terms, such as relational views and classification hierarchies. The user-defined terminology is stored in a data dictionary that maps it into the language of the database schema. A pattern is defined as a deductive rule expressed in user-defined terms that has a degree of uncertainty associated with it. Methods are presented for discovering interesting patterns based on abstracts which are summaries of the data expressed in the language of the user     

A functional approach to database updates

PFL is a functional database language in which functions are defined equationally and bulk data is stored using a special class of functions called selectors. It is a lazy language, supports higher-order functions, has a strong polymorphic type inference system, and allows new user-defined data types and values to be declared. All functions, types and values persist in a database. Functions can be written which update all aspects of the database: by adding data to selectors, by defining new equations, and by introducing new data types and values. PFL is “semi-referentially transparent”, in the sense that whilst updates are referentially opaque and are executed destructively, all evaluation is referentially transparent. Similarly, type checking is “semi-static” in the sense that whilst updates are dynamically type checked at run time, expressions are type checked before they are evaluated and no type errors can occur during their evaluation.

In this paper we examine the expressiveness of PFL with respect to updates, and illustrate the language by developing a number of general purpose update functions, including functions for restructuring selectors, for memoisation, and for generating unique system identifiers. We also provide a translation mechanism between Datalog programs and equations, and show how different Datalog evaluation strategies can be supported.     

Tightly-coupled spatial database features in the Odysseus/OpenGIS DBMS for high-performance

Conventional object-relational database management system (ORDBMS) vendors provide extension mechanisms for adding user-defined types and functions to their own DBMSs. Here, the extension mechanisms are implemented using a high-level (typically, SQL-level) interface. We call this mechanism loose-coupling. The advantage of loose-coupling is that it is easy to implement. However, it is not preferable for implementing new data types and operations in large databases when high performance is required. We have earlier proposed the tight-coupling architecture (Whang et al. 2002, 2005) to satisfy this requirement. In tight-coupling, new data types and operations are integrated into the core of the DBMS engine in the extensible type layer. Thus, they are supported in a consistent manner with high performance. This tight-coupling architecture is being used to incorporate information retrieval features and spatial database features into the Odysseus ORDBMS that has been under development at KAIST/AITrc for 19 years. In this paper, we introduce the tightly-coupled spatial database features of Odysseus/OpenGIS. By taking advantage of tight-coupling, Odysseus/OpenGIS provides excellent performance in processing spatial queries as well as flexible concurrency control and recovery on spatial data. We show the performance through extensive experiments. Finally, we present sample applications of a geographical information system (GIS) implemented using Odysseus/OpenGIS.     

The application of data types to database semantic integrity

Data type concepts are used to investigate the extent to which database semantic integrity can be defined and ensured through database structures. Database and datatype concepts are extended mutually to improve the semantic capabilities of both database models and data type systems and to resolve apparent discrepencies between databases and programming languages. To meet database needs, data structuring is developed to form an algebra of data types. A semantically rich database model is used to show that database models can be expressed in terms of data types. Finally, a schema specification language is presented to demonstrate the power of data type tools for the definition of database schemas and for the maintenance of database semantic integrity.     

Using online consumer reviews as a source for demographic recommendations: A case study using online travel reviews

Online consumer reviews play an important role in the decision to purchase services online, mainly due to the rich information source they provide to consumers in terms of evaluating “experience”-type products and services that can be booked using the Internet, with online travel services being a significant example. However, different types of travelers assess each quality indicator differently, depending on the type of travel they engage in, and not necessarily their cultural or age background (e.g. solo travelers, young couples with children etc.). In this study, we present architecture for a demographic recommendation system, based on a user-defined hierarchy of service quality indicator importance, and classification of traveler types. We use an algebraic approach to ascertain preferences from a large dataset that we obtained from the popular travel website Booking.com using a web crawler and compared with the customer-constructed preference matrix. Interestingly, the architecture of the evaluated recommendation system takes into account already defined demand characteristics of the hotels (such as the number of reviews of specific consumer types compared to the total number of reviews) in order to provide an example architecture for a recommendation system based on user-defined preference criteria.     

基于历史的用户自定义特征建模方法研究

在华中科技大学CAD中心自主开发的三维CAD系统———Intesolid中,提出了一种基于历史的用户自定义特征的建模方法,实现了基于用户自定义特征的设计意图的重构,提高了设计效率,体现了设计构件重用的思想,并简化了特征库的管理。     

Structure and software tools of AIDA

AIDA consists of a set of software tools to allow for fast development and easy-to-maintain Medical Information Systems. AIDA supports all aspects of such a system both during development and operation. It contains tools to build and maintain forms for interactive data entry and on-line input validation, a database management system including a data dictionary and a set of run-time routines for database access, and routines for querying the database and output formatting. Unlike an application generator, the user of AIDA may select parts of the tools to fulfill his needs and program other subsystems not developed with AIDA. The AIDA software uses as host language the ANSI-standard programming language MUMPS, an interpreted language embedded in an integrated database and programming environment. This greatly facilitates the portability of AIDA applications. The database facilities supported by AIDA are based on a relational data model. This data model is built on top of the MUMPS database, the so-called global structure. This relational model overcomes the restrictions of the global structure regarding string length. The global structure is especially powerful for sorting purposes. Using MUMPS as a host language allows the user an easy interface between user-defined data validation checks or other user-defined code and the AIDA tools. AIDA has been designed primarily for prototyping and for the construction of Medical Information Systems in a research environment which requires a flexible approach. The prototyping facility of AIDA operates terminal independent and is even to a great extent multi-lingual. Most of these features are table-driven; this allows on-line changes in the use of terminal type and language, but also causes overhead. AIDA has a set of optimizing tools by which it is possible to build a faster, but (of course) less flexible code from these table definitions. By separating the AIDA software in a source and a run-time version, one is able to write implementation-specific code which can be selected and loaded by a special source loader, being part of the AIDA software. This feature is also accessible for maintaining software on different sites and on different installations.     

Object-relational complex structures for XML storage

XML data can be stored in various database repositories, including Object-Relational Database (ORDB). Using an ORDB, we get the benefit of the relational maturity and the richness of Object-Oriented modeling, including various complex data types. These data types resemble the true nature of XML data and therefore, the conceptual semantic of XML data can be preserved. However, very often when the data is stored in an ORDB repository, they are treated as purely flat tables. Not only do we not fully utilize the facilities in current ORDB, but also we do not preserve the conceptual semantic of the XML data.In this paper, we propose novel methodologies to store XML data into new ORDB data structures, such as user-defined type, row type and collection type. Our methodology has preserved the conceptual relationship structure in the XML data, including aggregation, composition and association. For XML data retrieval, we also propose query classification based on the current SQL.Compared to the existing techniques, this work has several contributions. Firstly, it utilizes the newest features of ORDB for storing XML data. Secondly, it covers a full database design process, from the conceptual to the implementation phase. Finally, the proposed transformation methodologies maintain the conceptual semantics of the XML data by keeping the structure of the data in different ORDB complex structures.     

基于扩展存储过程的数据库加密技术研究

提出了一种基于扩展存储过程的数据库加密系统方案,并阐述了主要的功能模块.该方案应用扩展存储过程和UDF(用户自定义函数)对动态链接库进行封装,实现了加密功能,并对两级密钥采用智能卡进行存储,从而在不影响系统原有功能的基础上,提高了数据库系统的安全性.     

CASMI—A visualization tool for the World Stress Map database

The World Stress Map (WSM) project has compiled a global database of quality-ranked data records on the contemporary tectonic stresses in the Earth's crust. The WSM 2005 database release contains approximately 16 000 data records from different types of stress indicators such as earthquake focal mechanisms solutions, well bore breakouts, hydraulic fracturing and overcoring measurements, as well as quaternary fault-slip data and volcanic alignments. To provide a software tool for database visualization, analysis and interpretation of stress data as well its integration with other data records, we developed the program CASMI. This public domain software tool for Unix-like operating systems enables the selection of stress data records from the WSM database according to location, data quality, type of stress indicator, and depth. Each selected data record is visualized by a symbol that represents the type of stress indicator and the orientation of the maximum horizontal compressive stress. Symbol size is proportional to the quality of the data record, and the colour indicates different tectonic regimes. Stress maps can be produced in different geographical projections and high-quality output formats. CASMI also allows the integration of user-defined stress data sets and a wide range of other data such as topography, Harvard centroid moment tensors, polygons, text data, and plate motion trajectories. CASMI, including the WSM 2005 database release, can be requested free of charge from the project's website at http://www.world-stress-map.org/casmi. We present two stress map examples generated with CASMI ranging from plate-wide to regional scale: (1) A stress map of central Europe, that reveals the correlation of stress field orientation and relative plate motion. (2) The fan-shape stress pattern in North Germany.     

树型层次结构数据中遍历子树结点的方法

复杂关系数据的处理在数据库存储技术中具有重要的意义。在储层建模过程中高效的复杂数据处理方法将会大大提高建模的速度和准确性。阐述了存储过程和用户自定义函数相关概念,并以河流相随机游走储层建模相关数据为例,提出了通过使用存储过程和用户自定义函数对树型层次结构数据进行子结点递归遍历查询、存储的新方法,以便达到更高效的数据查询处理能力,最后通过SQL编程加以实现。实践表明,该方法具有很好的效果。     

适合地图数据库应用的可扩充数据模型研究

数据库地理信息系统（ＧＩＳ）的基础。复杂的ＧＩＳ应用要求它对用户定义的数据类型和操作具有扩充能力。该文针对用于ＧＩＳ的可扩充数据模型存在的问题。构造了一个适合地图数据库应用的可扩充数据模型。它以嵌套关系模型为基础。引入高阶算子定义操作。这些算子不但增强了代数操作的能力，而且利用它们能方便地扩充用户定义的几何操作、地图量算和几何变换。该模型具有较强的扩充能力，可以作主设计可扩充地图数据库系统的基础。     

基于Oracle Spatial的ITS空间数据库的实现及访问

在智能交通系统(Intelligent Transportatlon System，ITS)中，空间数据库的实现和访问至关重要。随着ITS的发展，基于传统的纯关系型数据库设计的空间数据库系统，其属性数据和空间数据是分开存储的，难以满足新型ITS在内容、数量、类型、关系等方面高速增长的要求。为此一些数据库厂商对传统的关系型数据库技术进行了扩充，并推出了一序列新的数据库产品，如Oracle公司的Oracle8i，真正实现了属性数据和空间数据统一存储和管理。该文结合实际ITS信息系统开发，分析了Oracle8i的数据库技术——Oracle Spatial，提出了一种基于Orackle Spatial的ITS空间数据库的实现及访问方法。     

基于MongoDB数据库的智能医疗分布式数据提取算法

智能医疗分布式数据提取受到数据节点数目的 影响,为了提高智能医疗分布式数据提取算法的性能,提出了基于MongoDB数据库的智能医疗分布式数据提取算法.在引入MongoDB数据库集群结构的基础上,构建了分布式数据的自定义词库,同时提取出分布式数据的标本名,完成了智能医疗分布式数据的预处理;利用建立分布式数据动态簇的步骤,...     

Temporal aggregation on user-defined granularities

Time-varying data play a major role in many applications, and, starting from the 80’s, they have been widely studied in temporal databases. In the last two decades, several researchers have shown that, to deal with many application domains, user-defined temporal granularities must be coped with. When data are stored at multiple user-defined temporal granularities, the task of defining proper conversion functions to aggregate data from an origin granularity (e.g., business days) to a task granularity (e.g., months) is of primary importance. However, current temporal database approaches mostly demand such a task to system administrators, or to specific applications, providing no methodology or general guideline to accomplish it. In this paper, we propose a general and application-independent methodology which, on the basis of the temporal relationship between two user-defined granularities, provides users with a set of conversion/aggregation functions between them, consistent with the telic vs. atelic character of the data to be aggregated. The correctness of the approach is also proved.     

Visualization of graphical data in a user-specified 2D space using a weighted Isomap method

We present a visualization system for exploring the high-dimensional graphical data, such as textures or 3D models, in 2D space using the dimensionality reduction method. To arrange high-dimensional data in a meaningful 2D space, we develop a novel semi-supervised dimensionality reduction method that can embed data of high dimension in a user-defined 2D coordinate system that is meaningful in terms of the properties of the data. This is achieved by modifying the Isomap method by weighting the data so that the resulting coordinates have no degeneracies and are orthogonal.     

A Flexible Ontology Reasoning Architecture for the Semantic Web

Knowledge-based systems in the semantic Web era can make use of the power of the semantic Web languages and technologies, in particular those related to ontologies. Recent research has shown that user-defined data types are very useful for semantic Web and ontology applications. The W3C semantic Web best practices and development working group has set up a task force to address this issue. Very recently, OWL-Eu and OWL-E, two decidable extensions of the W3C standard ontology language OWL DL, have been proposed to support customized data types and customized data type predicates, respectively. In this paper, we propose a flexible reasoning architecture for these two expressive semantic Web ontology languages and describe our prototype implementation of the reasoning architecture, based on the well-known FaCT DL reasoner, which witnesses the two key flexibility features of our proposed architecture: 1) It allows users to define their own data types and data type predicates based on built-in ones and 2) new data type reasoners can be added into the architecture without having to change the concept reasoner     

An approach to active spatial data mining based on statisticalinformation

Spatial data mining presents new challenges due to the large size of spatial data, the complexity of spatial data types, and the special nature of spatial access methods. Most research in this area has focused on efficient query processing of static data. This paper introduces an active spatial data mining approach that extends the current spatial data mining algorithms to efficiently support user-defined triggers on dynamically evolving spatial data. To exploit the locality of the effect of an update and the nature of spatial data, we employ a hierarchical structure with associated statistical information at the various levels of the hierarchy and decompose the user-defined trigger into a set of subtriggers associated with cells in the hierarchy. Updates are suspended in the hierarchy until their cumulative effect might cause the trigger to fire. It is shown that this approach achieves three orders of magnitude improvement over the naive approach that reevaluate the condition over the database for each update, while both approaches produce the same result without any delay. Moreover, this scheme can support incremental query processing as well