数据模型及其发展历程

数据库是数据管理的技术,是计算机学科的重要分支.经过近半个世纪的发展,数据库技术形成了坚实的理论基础、成熟的商业产品和广泛的应用领域.数据模型描述了数据库中数据的存储方式和操作方式.从数据组织形式,可以将数据模型分为结构化模型、半结构化模型、OLAP分析模型和大数据模型.20世纪60年代中后期到90年代初,结构化模型最早被提出,其主要包括层次模型、网状模型、关系模型和面向对象模型等.20世纪90年代末期,随着互联网应用和科学计算等复杂应用的快速发展,开始出现半结构化模型,包括XML模型、JSON模型和图模型等.21世纪,随着电子商务、商业智能等应用的不断发展,数据分析模型成为研究热点,主要包括关系型ROLAP和多维型MOLAP.2010年以来,随着大数据工业应用的快速发展,以NoSQL和NewSQL数据库系统为代表的大数据模型成为新的研究热点.对上述数据模型进行了综述,并选取每个模型的典型数据库系统进行了性能的分析.     

Deciding implication for functional dependencies in complex-value databases

Modern applications increasingly require the storage of data beyond relational structure. The challenge of providing well-founded data models that can handle complex objects such as lists, sets, multisets, unions and references has not been met yet in a completely satisfactory way. The success of such data models will greatly depend on the existence of automated database design techniques that generalise achievements from relational databases. In this paper, we study the implication problem of functional dependencies (FDs) in the presence of records, sets, multisets and lists. Database schemata are defined as nested attributes, database instances as nested relations and FDs are defined in terms of subattributes of the database schema. The expressiveness of FDs deviates fundamentally from previous approaches in different data models including the nested relational data model and XML.     

应用于GIS系统的多级时序数据模型

GIS中地理对象生命期的表示以及动态信息的表达需要时序数据的支持。一些基于关系数据库的GIS系统在实现时序数据时形成大量的数据冗余且难以维护数据的一致性。针对这些问题，结合GIS中对于时序精度的不同需求，提出了一种多级别时序数据模型。讨论了时间的相关概念，定义了一套基本的时间对象类型，提出并解释了对象级和属性级时序数据模型。基于面向对象数据库的实现显示，模型不仅满足了GIS中支持时序数据的需求，同时在存储开销、数据一致性等方面具有显著的优点。     

HGDBMS: a human genetics database management system

Human genetics research involves a large number of complex data sets naturally organized in hierarchical structures. Data collection is performed on different levels, e.g., the project level, pedigree level, individual level, and sample level. Different aspects of a study utilize different views of the data, requiring a flexible database management system (DBMS) which satisfies these different needs for data collection and retrieval. We describe HGDBMS, a comprehensive relational DBMS, implemented as an application of the GENISYS I DBMS, which allows embedding the hierarchical structure of pedigrees in a relational structure. The system's file structure is described in detail. Currently our Melanoma and Chromosome 17 map studies are managed with HGDBMS. Our initial experience demonstrates the value of a flexible system which supports the needs for data entry, update, storage, reporting, and analysis required during different phases of genetic research. Further developments will focus on the integration of HGDBMS with a human genetics expert system shell and analysis programs.     

Scientific articles recommendation with topic regression and relational matrix factorization

In this paper we study the problem of recommending scientific articles to users in an online community with a new perspective of considering topic regression modeling and articles relational structure analysis simultaneously. First, we present a novel topic regression model, the topic regression matrix factorization （tr-MF）, to solve the problem. The main idea of tr-MF lies in extending the matrix factorization with a probabilistic topic modeling. In particular, tr-MF introduces a regression model to regularize user factors through probabilistic topic modeling under the basic hypothesis that users share similar preferences if they rate similar sets of items. Consequently, tr-MF provides interpretable latent factors for users and items, and makes accurate predictions for community users. To incorporate the relational structure into the framework of tr-MF, we introduce relational matrix factorization. Through combining tr-MF with the relational matrix femtorization, we propose the topic regression collective matrix factorization （tr-CMF） model. In addition, we also present the collaborative topic regression model with relational matrix factorization （CTR-RMF） model, which combines the existing collaborative topic regression （CTR） model and relational matrix factorization （RMF）. From this point of view, CTR-RMF can be considered as an appropriate baseline for tr-CMF. Further, we demonstrate the efficacy of the proposed models on a large subset of the data from CiteULike, a bibliography sharing service dataset. The proposed models outperform the state-of-the-art matrix factorization models with a significant margin. Specifically, the proposed models are effective in making predictions for users with only few ratings or even no ratings, and support tasks that are specific to a certain field, neither of which has been addressed in the existing literature.     

Predicting Project Velocity in XP Using a Learning Dynamic Bayesian Network Model

Bayesian networks, which can combine sparse data, prior assumptions and expert judgment into a single causal model, have already been used to build software effort prediction models. We present such a model of an Extreme Programming environment and show how it can learn from project data in order to make quantitative effort predictions and risk assessments without requiring any additional metrics collection program. The model's predictions are validated against a real world industrial project, with which they are in good agreement.     

Constant false alarm rate detection in Pareto Type II clutter

Recent investigations have shown that the Pareto class of models provide a valid approximation for the statistical structure of the backscattering from the sea, for high resolution X-band maritime surveillance radar. This has stimulated the research and development of non-coherent radar detection processes for operation in such a clutter model environment. Using data from Defence Science and Technology Group's X-band radar the application of a Pareto Type I clutter model has been justified, which has facilitated the development of sliding window decision processes. However it has been found that when these detectors are applied to synthetic target detection in real data there are some issues resulting in substantial detection losses. In order to rectify this it is necessary to investigate the development of radar detection schemes under a Pareto Type II clutter model assumption. Using a transformation approach for radar detector design it will be shown that it is possible to construct detection processes that achieve the constant false alarm rate property with respect to the Pareto shape parameter, as in the Pareto Type I case, while requiring a priori knowledge of the Pareto scale parameter. Performance analysis includes application to real X-band clutter returns with synthetic target models.     

对象模式到关系模式转换的软件框架研究

数据在信息系统中的使用面临着这样一个困难，即数据存储是以关系模型为基础，而软件开发以对象模型来进行，造成了软件开发中数据访问技术的不和谐。在软件实现上陷入两种模式的转换工作，破坏面向对象语言的面向对象性，造成开发效率低下，代码重用率变低。提出并建立了一个软件框架，利用它提供的处于关系数据库和客户端之间的API来进行基于对象的数据库访问，充分发挥两种不同模式的优点，以提高软件开发的效率。     

Magnetic bubble memory structures for relational database management systems

Recently the concept of using magnetic bubble technology to store data and to perform logical operations has received considerable attention. By exploiting this hardware approach for relational database management systems, we introduce an efficient support for records permutation, sorting and searching for data. Actually these are substantial operations in relational data models.The organization of a relational data model in a magnetic bubbie memory is straightforward. In such systems the memory consists of loops, where each loop is capable of holding one record. Under the control of a switch, a loop can circulate the records or can hold them in position. However, as the number of switches in the memory system increases, the number of control lines becomes large and the model structure may lose its practical significance.In this investigation three different models of bubble memories are applied to a simple relational database example. The two basic operations of permuting records and searching for data are emphasized. For these models, some theoretical features, the essential characteristics and their applicability are pointed out.     

ArchIS: an XML-based approach to transaction-time temporal database systems

Effective support for temporal applications by database systems represents an important technical objective that is difficult to achieve since it requires an integrated solution for several problems, including (i) expressive temporal representations and data models, (ii) powerful languages for temporal queries and snapshot queries, (iii) indexing, clustering and query optimization techniques for managing temporal information efficiently, and (iv) architectures that bring together the different pieces of enabling technology into a robust system. In this paper, we present the ArchIS system that achieves these objectives by supporting a temporally grouped data model on top of RDBMS. ArchIS’ architecture uses (a) XML to support temporally grouped (virtual) representations of the database history, (b) XQuery to express powerful temporal queries on such views, (c) temporal clustering and indexing techniques for managing the actual historical data in a relational database, and (d) SQL/XML for executing the queries on the XML views as equivalent queries on the relational database. The performance studies presented in the paper show that ArchIS is quite effective at storing and retrieving under complex query conditions the transaction-time history of relational databases, and can also assure excellent storage efficiency by providing compression as an option. This approach achieves full-functionality transaction-time databases without requiring temporal extensions in XML or database standards, and provides critical support to emerging application areas such as RFID.     

3D Shape Matching and Inspection Using Geometric Features and Relational Learning

In this paper we consider the problem of matching 3D sensed data with models and inspection for defects where the correspondence between models and data needs to be solved in robust and efficient ways. We explore the use of machine learning (in particular, relational learning) as an efficient method for solving correspondence (and so, pose estimation) as well as automatically generating rules for acceptable shape variations from training data. As an additional but necessary issue, we also consider the use of view-independent covariance methods for the extraction of surface features used to determine shape signatures which correspond to curvature-like surface attributes. Such features are utilized in the relational learning model.     

A polynomial relational class of binary CSP

Finding a solution to a constraint satisfaction problem (CSP) is known to be an NP-hard task. Considerable effort has been spent on identifying tractable classes of CSP, in other words, classes of constraint satisfaction problems for which there are polynomial time recognition and resolution algorithms. In this article, we present a relational tractable class of binary CSP. Our key contribution is a new ternary operation that we name mjx. We first characterize mjx-closed relations which leads to an optimal algorithm to recognize such relations. To reduce space and time complexity, we define a new storage technique for these relations which reduces the complexity of establishing a form of strong directional path consistency, the consistency level that solves all instances of the proposed class (and, indeed, of all relational classes closed under a majority polymorphism).     

基于GIS的灾害应急管理系统的数据集成研究

常用的关系数据库缺乏直接管理空间数据的成熟通用方案。借助于空间数据库引擎(SDE)技术,扩展关系数据库功能,使关系数据库成为业务数据和空间数据的统一存储管理平台,并在此基础上建立基于Geodatabase模型的灾害应急管理系统数据库,对系统数据整体结构进行研究设计,解决了空间数据和业务数据的关联问题,实现灾害应急管理系统空间数据和业务数据的无缝集成和一体化存储。该方法已在省级应急管理信息示范平台上成功应用。     

一种数据仓库的多维数据模型

数据模型是数据仓库研究的核心问题之一.很多研究表明,传统数据模型(如实体联系模型和关系模型)不能有效地表示数据仓库的数据结构和语义,也难以有效地支持联机分析处理(on-line analysis processing,简称OLAP).最近,人们提出了几种多维数据模型.但是,这些多维数据模型在表示数据仓库的复杂数据结构和语义以及OLAP操作方面仍显不足.该文以偏序和映射为基础,提出了一种新的多维数据模型.该数据模型能够充分表达数据仓库的复杂数据结构和语义,并提供一个以OLAP操作为核心的操作代数,支持层次结构间的复杂聚集操作序列,能够有效地支持OLAP应用.该数据模型支持聚集函数约束的概念,提供了表示层次结构间聚集函数约束的机制.     

KGDB: Knowledge Graph Database System with Unified Model and Query Language

Knowledge graph is an important cornerstone of artificial intelligence, which currently has two main data models: RDF graphs and property graphs. There are several query languages on these two data models, including SPARQL on RDF graphs and Cypher on property graphs. Over the last decade, various communities have developed different data management methods for RDF graphs and property graphs. Inconsistent data models and query languages hinder the wider application of knowledge graphs. In this paper, we propose a knowledge graphy database (KGDB) system with unified data model and query language. (1) We work out a unified storage scheme based on the relational model that supports the efficient storage of RDF graphs and property graphs, catering to the smooth storage and query of knowledge graph data. (2) The characteristic set-based clustering is used in KGDB for the storage of typeless entities. (3) It realizes the interoperability of SPARQL and Cypher by enabling them to operate on the same knowledge graph. Extensive experiments on real-world datasets and synthetic datasets reveal that KGDB is more efficient than existing knowledge graph database management systems in storage management and query efficiency. KGDB saves 30% of the storage space on average compared with gStore and Neo4j. In addition, KDGB is two orders of magnitude faster than gStore and Neo4j in the query of the real-world datasets, seen from experiments on the query of basic graph pattern matching.     

对象模式到关系模式转换的软件框架研究

数据在信息系统中的使用面临着这样一个困难,即数据存储是以关系模型为基础,而软件开发以对象模型来进行,造成了软件开发中数据访问技术的不和谐。在软件实现上陷入两种模式的转换工作,破坏面向对象语言的面向对象性,造成开发效率低下,代码重用率变低。提出并建立了一个软件框架,利用它提供的处于关系数据库和客户端之间的API来进行基于对象的数据库访问,充分发挥两种不同模式的优点,以提高软件开发的效率。     

Enhanced spatiotemporal relational probability trees and forests

Many real world domains are inherently spatiotemporal in nature. In this work, we introduce significant enhancements to two spatiotemporal relational learning methods, the spatiotemporal relational probability tree and the spatiotemporal relational random forest, that increase their ability to learn using spatiotemporal data. We enabled the models to formulate questions on both objects and the scalar and vector fields within and around objects, allowing the models to differentiate based on the gradient, divergence, and curl and to recognize the shape of point clouds defined by fields. This enables the model to ask questions about the change of a shape over time or about its orientation. These additions are validated on several real-world hazardous weather datasets. We demonstrate that these additions enable the models to learn robust classifiers that outperform the versions without these new additions. In addition, analysis of the learned models shows that the findings are consistent with current meteorological theories.     

Selection of indexes to memory-resident entities for semantic datamodels

A variation of the index selection problem for an extended relational model when all encoding of information is memory resident is discussed. The data model is the relational model extended in two ways that are common with semantic data models. One consequence of memory residence is that the search space of possible indexes is enlarged to the extent that previous methods requiring some consideration of each possibility are no longer possible. An instance of the index selection problem that includes a set of partial match queries in addition to the input schema is given. It is assumed that the set is determined by an initial phase of query optimization when applied to a fixed set of more general forms of queries that characterize the way in which information is accessed for an application. An initial choice of indexes is made, only considering their suitability for answering the partial match queries     

基于XML Schema的XML存储

文章介绍了一个在关系数据库中,基于XMLSchema的XML存储方案。描述了一个以树模型为模型,XMLSchema为模式的XML数据库的存储系统。首先给出了在关系数据库中存储XMLSchema的方法,在此基础上,又给出了存储XML文档的方法。与通常的XML分解存储方案不同之处在于,在该方案中,XMLSchema被保存到数据库中,未作模式映射,避免了模式映射通常会带来的数据丢失和数据要分散到多个关系表中的问题;对XML文档的存储和查询都是基于XMLSchema的;并且由于所有基于同一个模式的XML文档共享该模式的结构,不必对结构信息进行重复存储,减少了存储空间;最后,由于我们为每一个元素赋予了一个唯一ID值,在进行查询的时候,可以利用该ID值进行定位,具有和XPath表达式相同的作用,但是更为简便。     

Recognizing shape features in solid models

A procedure for defining and recognizing shape features 3-D solid models is presented in which a shape feature is defined as a single face or a set of continuous faces possessing certain characteristic facts in topology and geometry. The system automatically extracts these facts from an example shape feature interactively indicated by the user. The resulting representation of the shape feature can be interactively edited and parameterized. Graph matching accomplishes feature recognition. The system searches the solid model for B-rep subgraphs with the same characteristic facts as the shape feature to be recognized. When the system recognizes a shape feature, it removes the geometry associated with the feature from the original solid model to produce a simpler solid model. It then examines the simpler solid model to determine whether additional features have been revealed. The process repeats until no additional features are found