An evaluation between Bloom Filter join and PERF join in Distributed Query Processing

Nowadays, with the explosion of information and the telecommunication era\u27s coming, more and more huge applications encourage decentralization of data while accessing data from different sites [HFB00]. The process of retrieving data from different sites called Distributed Query Processing. The objective of distributed query optimization is to find the most cost-effective of executing query across the network [OV99]. Semijoin [BC81] [BG+81] is known as an effective operator to eliminate the tuples of a relation which are not contributive to a query. 2-way semijoin [KR87] is an extended version of semijoin which not only performs forward reduction like traditional semijoin does, but also provides backward reduction always in cost-effective way. Bloom Filter[B70] and PERF [LR95] are 2 filter based techniques which use a bit vector to represent of the original join attributes projection during the data transmission. Compare with generating a bit array with hash function in bloom filter, Perf join is based on the tuples scan order to avoid losing information caused by hash collision. In the thesis, we will apply both bloom filter and pert on 2-way semijoin algorithms to reduce transmission cost of distributed queries. Performance of propose algorithms will compare against each others and IFS (Initial Feasible Solution) through amount of experiments. \u27Keywords:\u27 Distributed Query Processing, Semijoin, Bloom Filter, Perf Join

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

AstroGrid-D: Grid Technology for Astronomical Science

We present status and results of AstroGrid-D, a joint effort of astrophysicists and computer scientists to employ grid technology for scientific applications. AstroGrid-D provides access to a network of distributed machines with a set of commands as well as software interfaces. It allows simple use of computer and storage facilities and to schedule or monitor compute tasks and data management. It is based on the Globus Toolkit middleware (GT4). Chapter 1 describes the context which led to the demand for advanced software solutions in Astrophysics, and we state the goals of the project. We then present characteristic astrophysical applications that have been implemented on AstroGrid-D in chapter 2. We describe simulations of different complexity, compute-intensive calculations running on multiple sites, and advanced applications for specific scientific purposes, such as a connection to robotic telescopes. We can show from these examples how grid execution improves e.g. the scientific workflow. Chapter 3 explains the software tools and services that we adapted or newly developed. Section 3.1 is focused on the administrative aspects of the infrastructure, to manage users and monitor activity. Section 3.2 characterises the central components of our architecture: The AstroGrid-D information service to collect and store metadata, a file management system, the data management system, and a job manager for automatic submission of compute tasks. We summarise the successfully established infrastructure in chapter 4, concluding with our future plans to establish AstroGrid-D as a platform of modern e-Astronomy.Comment: 14 pages, 12 figures Subjects: data analysis, image processing, robotic telescopes, simulations, grid. Accepted for publication in New Astronom

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Data integration for biological network databases: MetNetDB labeled graph model and graph matching algorithm

To understand the cellular functions of genes requires investigating a variety of biological data, including experimental data, annotation from online databases and literatures, information about cellular interactions, and domain knowledge from biologists. These requirements demand a flexible and powerful biological data management system. MetNetDB is the biological database component of the MetNet platform (http://metnetdb.org/), a software platform for Arabidopsis system biology. This work describes a labeled graph model that addresses the challenges associated with biological network databases, and discusses the implementation of this model in MetNetDB. MetNetDB integrates most recent data from various sources, including biological networks, gene annotation, metabolite information, and protein localization data. The integration contains four steps: data model transformation and integration; semantic mapping; data conversion and integration; and conflict resolution. MetNetDB is established as a labeled graph model. The graph structure supports network data storage and application of graph analysis algorithm. The node and edge labels have the same extension capability as object data model. In addition, rules are used to guarantee the biological network data integrity; operations are defined for graph edit and comparison. To facilitate the integration of network data, which is often inaccurate or incomplete, a subgraph extraction algorithm is designed for MetNetDB. This algorithm allows subgraph querying based on user-specified biomolecules. Both exact matching and approximate matching with biomolecules in networks are supported. The similarity among biomolecules is inferred from expression patterns, gene ontology, chemical ontology, and protein-gene relationships. Combined with the implementation of Messmer\u27s approximate subgraph isomorphism algorithm, MetNetDB supports exact and approximate graph matching. Based on the MetNetDB labeled graph model and the graph matching algorithms, the MetNetDB curator tool is built with several innovative features, including active biological rule checking during network curation, tracking data change history, and a biologist-friendly visual graph query system

A Framework for Top-K Queries over Weighted RDF Graphs

abstract: The Resource Description Framework (RDF) is a specification that aims to support the conceptual modeling of metadata or information about resources in the form of a directed graph composed of triples of knowledge (facts). RDF also provides mechanisms to encode meta-information (such as source, trust, and certainty) about facts already existing in a knowledge base through a process called reification. In this thesis, an extension to the current RDF specification is proposed in order to enhance RDF triples with an application specific weight (cost). Unlike reification, this extension treats these additional weights as first class knowledge attributes in the RDF model, which can be leveraged by the underlying query engine. Additionally, current RDF query languages, such as SPARQL, have a limited expressive power which limits the capabilities of applications that use them. Plus, even in the presence of language extensions, current RDF stores could not provide methods and tools to process extended queries in an efficient and effective way. To overcome these limitations, a set of novel primitives for the SPARQL language is proposed to express Top-k queries using traditional query patterns as well as novel predicates inspired by those from the XPath language. Plus, an extended query processor engine is developed to support efficient ranked path search, join, and indexing. In addition, several query optimization strategies are proposed, which employ heuristics, advanced indexing tools, and two graph metrics: proximity and sub-result inter-arrival time. These strategies aim to find join orders that reduce the total query execution time while avoiding worst-case pattern combinations. Finally, extensive experimental evaluation shows that using these two metrics in query optimization has a significant impact on the performance and efficiency of Top-k queries. Further experiments also show that proximity and inter-arrival have an even greater, although sometimes undesirable, impact when combined through aggregation functions. Based on these results, a hybrid algorithm is proposed which acknowledges that proximity is more important than inter-arrival time, due to its more complete nature, and performs a fine-grained combination of both metrics by analyzing the differences between their individual scores and performing the aggregation only if these differences are negligible.Dissertation/ThesisM.S. Computer Science 201