多媒体对象查询语言及其查询处理

文章研究了多媒体数据库的查询需求,提出结构化的多媒体对象查询语言MOQL(multi-media object query language).它能够支持基于类型、结构特征、同步关系、时态关系和内容信息的多媒体查询.以DB2数据库为存储机制,定义了一组代数算子和变换规则,利用它们可以将用户定义的MOQL查询变换为代数表达式,进行代数优化,并将代数查询表达式转换为能够在DB2数据库上运行的DB2SQL和C++查询过程.     

基于多媒体图像数据的查询检索

本文对多媒体图像数据提出一套较灵活的存储、索引查询方式。针对多媒体数据库信息查询通用性的问题，构建了一个较为集成的特征库。针对高维数据查询效率问题，将基于内容、颜色、距离和形状的匹配算法与FNN算法及查找聚类重心的加速检索算法集成于查询检索引擎IRE，使查询更高效、应用更广泛。     

基于Interbase的多媒体数据管理系统

一个基于InterBase的多媒体数据综合管理系统。该系统通过Delphi编程形成一个集多媒体数据存储、浏览、查询、点播、导出和加工处理为一体的操作平台,并通过API调用增强了对新媒体类型的处理能力。     

一种基于预测的目标跟踪传感器网络数据近似存储查询策略

节能问题是传感器网络设计的一个关键问题,在对目标跟踪传感器网络中的目标位置信息进行存储和查询处理时,也必须通过较低的通信代价来降低消息复杂度,从而达到节能的目的.分析了现存的一些数据存储和数据分发的方法,针对EASE方法查询误差较大的问题,提出了基于预测的近似查询的存储策略(Prediction-based Energy-conserving Approximate StoragE,P-EASE),并就EASE方法查询未考虑选择恰当的存储节点的问题,提出了一种基于地理位置的择优查询(Optimal Query)策略.使用半随机步行移动模型和随机路点移动模型进行了仿真实验,比较了P-EASE与EASE算法的消息量、消息复杂度、平均消息复杂度和查询误差.仿真实验证明,P-EASE比EASE查询更精确、能量更高效.     

基于海量存储系统AS10000的浪潮数字图书馆存储解决方案

正基于浪潮海量存储系统AS10000构建的数字图书馆业务系统,在保证高速I/O通信带宽的前提下提供海量的数据存储空间。数字图书馆概况数字图书馆是用数字技术处理和存储各种图文并茂文献的图书馆,实质上是一种多媒体制作的分布式信息系统。它把各种不同载体、不同地理位置的信息资源用数字技术存贮,以便于跨越区域、面向对象的网络查询和传播。它涉及信息资源加工、存储、检索、传输和利     

多媒体数据库系统

本文旨在讨论多媒体数据库系统的特征。文中首先分析多媒体数据的特点及其对数据库管理系统的需求，然后，分别探讨多媒体数据库系统的关键技术问题：体系结构，物理存储模型，多媒体数据模型，多媒体对象的时空合成，查询处理和用户接口技术。     

面向多媒体文档的多通道检索系统

提出和实现了一个面向多媒体文档的多通道(对应多种模态,如文本、图像、视频等)检索系统.系统定义了一个新的用来描述多媒体文档内容的框架,该框架不但提取出多媒体文档在各通道下的基于内容的底层特征,而且还记录下多媒体文档中不同多媒体对象间的链接关系.同时,提出一种基于图模型的交叉参照知识库,用来存储从链接关系中挖掘出的多媒体对象间的语义关系,通过一个有效的语义上下文分析算法,在检索过程中计算每个对象与查询的语义相似度.语义上下文分析算法不仅使得基于内容的多媒体信息检索中考虑了多媒体对象的语义信息,同时支持用户通过通道切换的方式进行相关反馈,提供了一种较为灵活的查询模式.实验表明在交叉参照知识库基础上,该系统还能够有效地提高基于内容的多媒体对象的检索性能(如内容覆盖率等).     

XML数据磁盘存储结构LDF&B及其查询处理方法

提出了一种基于F&B索引的XML数据磁盘存储结构LDF&B,并给出其上的一系列查询处理算法.一方面,它摒除了传统存储方法中相同的值信息重复存储带来大量冗余的问题;另一方面,引入的同带索引与反向索引结构大大减少了树的遍历操作和中间结果集的大小,提高了查询处理效率.理论分析与实验结果均表明,提出的算法高效地实现了disk-based F&B中无法处理的含值的查询,分支查询处理效率也得到显著提高,并具有较好的伸缩性.     

基于动态镜像的实时数据仓库存取预处理技术研究

实时数据仓库是数据仓库技术的重要分支,而实时数据查询和实时数据导入引发的查询竞争问题一直是实时数据仓库技术研究的重点之一。查询竞争问题严重影响了查询分析的精度和效率,还降低了数据仓库的性能。提出了一种在数据仓库外部构建动态存储区域的方法,它采用动态镜像技术,有效地缓解查询竞争问题。同时,为了提高实时OLAP上的查询分析操作的性能,提出了蝇量级物化方法及蝇量级物化下的表连接算法FWMJoin(Fly-Weight Materialization Join)。基于TPC-H基准的实时数据仓库测试系统,针对动态镜像技术下的动态存储区域的OLAP性能进行分析与评估,并对实验结果进行总结。     

查询感知的关系-图数据库自适应存储技术研究

在大数据时代,针对不同场景下如何保持数据高效查询受到持续关注,但是对通过改进数据的存储管理技术来提高查询效率还有待进一步研究。因此,结合图数据结构类型多变、应用场景丰富、数据集价值高等优势,提出了一种利用关系-图数据模型协同存储数据的模式,并设计了用户查询感知的自适应存储优化技术来解决多数据模型的数据存储冗余优化问题。通过分析不同引擎处理不同的查询得出每种引擎对应的不同查询性能和多数据模型存储存在的数据冗余问题,提出用户查询感知的自适应存储技术。再结合用户历史查询及查询特点,利用基于启发式规则的优化算法完成多数据模型的数据存储优化。     

存储容量可扩展区块链系统的高效查询模型

区块链技术是目前计算机领域的研究热点,其实现了去中心化,并且能够安全地存储数字信息,有效降低现实经济的信任成本.提出一种区块链存储容量可扩展模型的高效查询方法——ElasticQM.此查询模型由用户层、查询层、存储层和数据层这4个模块组成.在用户层,模型将查询结果缓存,加快再次查询相同数据时的查询速度;在查询层,模型采用容量可扩展区块链模型的全局查询优化算法,增加了查询超级节点、查询验证节点和查询叶子节点这3种节点角色,提高了查询效率;在存储层,模型改进了区块链的容量可扩展模型ElasticChain的数据存储过程,实现了存储的可扩展性,并减少了占用的存储空间;在数据层,提出一种基于B-M树的区块链存储结构,并给出了B-M树的建立算法和基于B-M树的查找算法,基于B-M树的存储结构,区块链会在进行块内局部查找时提高区块链的查询速度.最后,通过在多节点不同数据量的区块链中查询的实验结果表明,ElasticQM查询方法具有高效的查询效率.     

An efficient query optimization strategy for spatio-temporal queries in video databases

The interest for multimedia database management systems has grown rapidly due to the need for the storage of huge volumes of multimedia data in computer systems. An important building block of a multimedia database system is the query processor, and a query optimizer embedded to the query processor is needed to answer user queries efficiently. Query optimization problem has been widely studied for conventional database systems; however it is a new research area for multimedia database systems. Due to the differences in query processing strategies, query optimization techniques used in multimedia database systems are different from those used in traditional databases. In this paper, a query optimization strategy is proposed for processing spatio-temporal queries in video database systems. The proposed strategy includes reordering algorithms to be applied on query execution tree. The performance results obtained by testing the reordering algorithms on different query sets are also presented.     

Cross-media retrieval using query dependent search methods

The content-based cross-media retrieval is a new type of multimedia retrieval in which the media types of query examples and the returned results can be different. In order to learn the semantic correlations among multimedia objects of different modalities, the heterogeneous multimedia objects are analyzed in the form of multimedia document (MMD), which is a set of multimedia objects that are of different media types but carry the same semantics. We first construct an MMD semi-semantic graph (MMDSSG) by jointly analyzing the heterogeneous multimedia data. After that, cross-media indexing space (CMIS) is constructed. For each query, the optimal dimension of CMIS is automatically determined and the cross-media retrieval is performed on a per-query basis. By doing this, the most appropriate retrieval approach for each query is selected, i.e. different search methods are used for different queries. The query dependent search methods make cross-media retrieval performance not only accurate but also stable. We also propose different learning methods of relevance feedback (RF) to improve the performance. Experiment is encouraging and validates the proposed methods.     

数据网格环境下的多媒体资源检索

提出一种数据网格环境下的多媒体资源检索方法。通过设计分层结构的虚拟资源空间管理非结构化异构资源,当网格查询节点收到检索请求时,根据虚拟集和元数据等信息进行信息过滤,得到候选集及相应的结构化特征和语义标注,执行节点对候选集进行相似度求精和排序运算,并将排序结果返回查询节点。仿真结果表明,该方法具有较好的时间性能和较高的查准率。     

Query quality refinement in singular value decomposition to improve genetic algorithms for multimedia data retrieval

With the development of internet and availability of multimedia data capturing devices, the size of Multimedia Digital Database (MDD) collection is increasing rapidly. The complex data presented by such systems do not have the total ordering property presented by the traditional data handled by Database Management Systems (DBMSs). The quality of the search experience in such systems is also normally a big challenge since users from various domains require efficient data searching, browsing and retrieval tools. This has triggered an important research topic in Multimedia information retrieval concerning efficient and effective image similarity search. Modern search algorithms are fast and effective on a wide range of problems, but on MDD with a large number of parameters and observations, manipulations of large matrices, storage and retrieval of large amounts of information may render an otherwise useful method slow or inoperable. The focus of this work is the application of image enhancement technique, using histogram equalization, to the images retrieved using singular value decomposition (SVD). SVD is a linear algebra technique used for discovering correlations within data. The approach, herein referred to as query quality refinement (QQR) technique, improves the image similarity search result, and when incorporated with genetic algorithms further optimizes the search. These beneficial applications can be extended to other different types of multimedia data in various areas such as the P2P and WiMAX networks.     

An efficient continuous k-nearest neighbor query processing scheme for multimedia data sharing and transmission in location based services

The continuous k-nearest neighbor query is one of the most important query types to share multimedia data or to continuously identify transportable users in LBS. Various methods have been proposed to efficiently process the continuous k-NN query. However, most of the existing methods suffer from high computation time and larger memory requirement because they unnecessarily access cells to find the nearest cells on a grid index. Furthermore, most methods do not consider the movement of a query. In this paper, we propose a new processing scheme to process the continuous k nearest neighbor query for efficiently support multimedia data sharing and transmission in LBS. The proposed method uses the patterns of the distance relationships among the cells in a grid index. The basic idea is to normalize the distance relationships as certain patterns. Using this approach, the proposed scheme significantly improves the overall performance of the query processing. It is shown through various experiments that our proposed method outperforms the existing methods in terms of query processing time and storage overhead.

     

Architecture of the ORION next-generation database system

Various architectural components of ORION-1 and ORION-1SX are described and a review of the current implementation is provided. The message handler receives all messages sent to the ORION system. The object subsystem provides high-level data management functions, including query optimization, schema management, long data management (including text search) and support for versionable objects, composite objects, and multimedia objects. The transaction management subsystem coordinates concurrent object accesses and provides recovery capabilities. The storage subsystem manages persistent storage of objects and controls the flow of objects between the secondary storage device and main memory buffers. In ORION-1, all subsystems reside in one computer. The ORION-1SX architecture is significantly different from ORION-1 in the management of shared data structures and distribution of these subsystems and their components     

Indexing the solution space: a new technique for nearest neighborsearch in high-dimensional space

Similarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of high-dimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore precompute the result of any nearest-neighbor search which corresponds to a computation of the Voronoi cell of each data point. In a second step, we store conservative approximations of the Voronoi cells in an index structure efficient for high-dimensional data spaces. As a result, nearest neighbor search corresponds to a simple point query on the index structure. Although our technique is based on a precomputation of the solution space, it is dynamic, i.e., it supports insertions of new data points. An extensive experimental evaluation of our technique demonstrates the high efficiency for uniformly distributed as well as real data. We obtained a significant reduction of the search time compared to nearest neighbor search in other index structures such as the X-tree     

Intelligent multimedia data: data + indices + inference

In this paper, we present a novel approach for multimedia data indexing and retrieval that is machine independent and highly flexible for sharing multimedia data across applications. Traditional multimedia data indexing and retrieval problems have been attacked using the central data server as the main focus, and most of the indexing and query-processing for retrieval are highly application dependent. This precludes the use of created indices and query processing mechanisms for multimedia data which, in general, have a wide variety of uses across applications. The approach proposed in this paper addresses three issues: 1. multimedia data indexing; 2. inference or query processing; and 3. combining indices and inference or query mechanism with the data to facilitate machine independence in retrieval and query processing. We emphasize the third issue, as typically multimedia data are huge in size and requires intra-data indexing. We describe how the proposed approach addresses various problems faced by the application developers in indexing and retrieval of multimedia data. Finally, we present two applications developed based on the proposed approach: video indexing; and video content authorization for presentation.     

城市交通智能咨询系统的设计与实现

分析了国内外城市交通咨询的现状；给出了城市交通咨询软件实现的空间数据结构；提出了基于网络拓扑的智能咨询搜索算法；运用人工智能、地理信息、多媒体等技术，实现了城市交通的智能咨询。测试表明，该系统具有速度快、多媒体显示、维护方便、通用性强等特点。