Ranked Keyword Search on Graph-Structured Databases ab 58.99 € als Taschenbuch: Techniques for User-friendly High Quality and Efficient Information Discovery on Data Graphs. Aus dem Bereich: Bücher, Ratgeber, Computer & Internet,
This book represents a new model of meta-search engine. Existing meta-search engines do not have their own databases for indexing purpose. They directly send request to individual fixed number of search engines for user search text and retrieves aggregate single list of result by eliminating duplicates. They are using their own ranking formulas to display retrieved links in some specific order. Moreover, existing meta-search engine uses optimization techniques for revenue and provides paid listings. Hence, ranking order is less reliable. In new model of a meta-search engine the concept of database has been introduced for indexing purpose. If relevant search keywords already available in keyword database, then it retrieves results from it in an efficient way. This helps to improve response time of meta-search engine and eliminates the meta-search engine time-out problem.
Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships, and a XML database can be represented as a graph with XML elements as nodes and containment or ID-IDREF edges as hyperlinks. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web. One of the key advantages of keyword search querying is its simplicity - users do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data. Since the keyword search query interface is very flexible, queries may not always be precise and can potentially return a large number of query results, especially in large document collections. Consequently, an important requirement for keyword search is to rank the query result. The goal of this book is to present various search techniques.
With the emergence of the World Wide Web and online businesses, business XML databases are increasingly being queried directly by users. This book focuses on three aspects related to querying of XML data, as follows: (1) Improving accuracy of XML keyword queries by modeling the contexts of XML elements. Most current research is focused on building relationships between data elements based solely on their labels and proximity to one another, while overlooking the contexts of the elements. This may lead to erroneous results. This book presents different context-driven techniques that avoid this problem. (2) Enhancing XML-based personalized search by using group profiling to determine individual preferences. Group profiling can be an efficient retrieval mechanism, where a user profile is inferred from the profile of the social groups to which the user belongs. A social group is defined based on ethnic, cultural, religious, or other characteristics. (3) Improving performance of distributed XML querying by caching of frequently-used query results. For each of these areas, we developed formal concepts and algorithms that lead to the improved accuracy and performance.
Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web. One of the key advantages of keyword search querying is its simplicity users do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data. This book presents techniques for user-friendly, high quality and efficient searching of graph structured databases. The methods and techniques presented should help shed some light on this new and exciting field, and should especially be useful to professionals in Computer science or Information technology fields, or anyone else who may be considering a research career in the area of Databases and Information Retrieval.
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. "Content-based" means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image. The term 'content' in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because most web based image search engines rely purely on metadata and this produces a lot of garbage in the results. Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image. This book discusses the novel idea of sectorization of the planes conceptualized out of the transformed images for feature extraction. The new idea of wavelet generation from the basic mathematical transforms has been used for the feature extraction as well.
This book constitutes the refereed proceedings of the 14th International Symposium on Spatial and Temporal Databases, SSTD 2015, held in Hong Kong, China, in August 2015.The 24 revised full papers together with 8 demos presented were carefully reviewed and selected from 64 submissions. The conference program has the scope on following subjects: reachability query and path query, reverse query and indexing, navigation and routing, trajectory analysis, spatio-temporal approaches, privacy and matching, similarity search and pattern, keyword and pattern.
This two volume set LNCS 9827 and LNCS 9828 constitutes the refereed proceedings of the 27th International Conference on Database and Expert Systems Applications, DEXA 2016, held in Porto, Portugal, September 2016. The 39 revised full papers presented together with 29 short papers were carefully reviewed and selected from 137 submissions. The papers discuss a range of topics including: Temporal, Spatial, and High Dimensional Databases, Data Mining, Authenticity, Privacy, Security, and Trust, Data Clustering, Distributed and Big Data Processing, Decision Support Systems, and Learning, Data Streams, Data Integration, and Interoperability, Semantic Web, and Data Semantics, Social Networks, and Network Analysis, Linked Data, Data Analysis, NoSQL, NewSQL, Multimedia Data, Personal Information Management, Semantic Web and Ontologies, Database and Information System Architectures, Query Answering and Optimization, Information Retrieval, and Keyword Search, Data Modelling, and Uncertainty.