Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, Thomas J. Watson School of Engineering and Applied Science, 2007. / Includes bibliographical references.

Thesis (M.Sc.). / Written for the School of Computer Science. Title from title page of PDF (viewed 2009/06/25). Includes bibliographical references.

Thesis (Ph.D) - Swinburne University of Technology, Faculty of Information & Communication Technologies, 2009. / A dissertation submitted to the Faculty of Information and Communication Technologies, Swinburne University of Technology in partial fulfillment of the requirements for the degree of Doctor of Philosophy, 2009. Typescript. "August 2009". Bibliography p. 161-171.

Thesis (M.S.)--The University of North Carolina at Greensboro, 2009. / Directed by Fereidoon Sadri; submitted to the Dept. of Computer Science. Title from PDF t.p. (viewed May 25, 2010). Includes bibliographical references (p. 47).

abstract: Genomic and proteomic sequences, which are in the form of deoxyribonucleic acid (DNA) and amino acids respectively, play a vital role in the structure, function and diversity of every living cell. As a result, various genomic and proteomic sequence processing methods have been proposed from diverse disciplines, including biology, chemistry, physics, computer science and electrical engineering. In particular, signal processing techniques were applied to the problems of sequence querying and alignment, that compare and classify regions of similarity in the sequences based on their composition. However, although current approaches obtain results that can be attributed to key biological properties, they require pre-processing and lack robustness to sequence repetitions. In addition, these approaches do not provide much support for efficiently querying sub-sequences, a process that is essential for tracking localized database matches. In this work, a query-based alignment method for biological sequences that maps sequences to time-domain waveforms before processing the waveforms for alignment in the time-frequency plane is first proposed. The mapping uses waveforms, such as time-domain Gaussian functions, with unique sequence representations in the time-frequency plane. The proposed alignment method employs a robust querying algorithm that utilizes a time-frequency signal expansion whose basis function is matched to the basic waveform in the mapped sequences. The resulting WAVEQuery approach is demonstrated for both DNA and protein sequences using the matching pursuit decomposition as the signal basis expansion. The alignment localization of WAVEQuery is specifically evaluated over repetitive database segments, and operable in real-time without pre-processing. It is demonstrated that WAVEQuery significantly outperforms the biological sequence alignment method BLAST for queries with repetitive segments for DNA sequences. A generalized version of the WAVEQuery approach with the metaplectic transform is also described for protein sequence structure prediction. For protein alignment, it is often necessary to not only compare the one-dimensional (1-D) primary sequence structure but also the secondary and tertiary three-dimensional (3-D) space structures. This is done after considering the conformations in the 3-D space due to the degrees of freedom of these structures. As a result, a novel directionality based 3-D waveform mapping for the 3-D protein structures is also proposed and it is used to compare protein structures using a matched filter approach. By incorporating a 3-D time axis, a highly-localized Gaussian-windowed chirp waveform is defined, and the amino acid information is mapped to the chirp parameters that are then directly used to obtain directionality in the 3-D space. This mapping is unique in that additional characteristic protein information such as hydrophobicity, that relates the sequence with the structure, can be added as another representation parameter. The additional parameter helps tracking similarities over local segments of the structure, this enabling classification of distantly related proteins which have partial structural similarities. This approach is successfully tested for pairwise alignments over full length structures, alignments over multiple structures to form a phylogenetic trees, and also alignments over local segments. Also, basic classification over protein structural classes using directional descriptors for the protein structure is performed. / Dissertation/Thesis / Ph.D. Electrical Engineering 2011

CRUZ, Lívia Almada. Consultas kNN em redes dependentes do tempo. 2013. 75 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2013. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-11T18:24:05Z No. of bitstreams: 1 2013_dis_lacruz.pdf: 6954650 bytes, checksum: fbf7280f2f781976bae6e4474c2c16c6 (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-20T11:52:58Z (GMT) No. of bitstreams: 1 2013_dis_lacruz.pdf: 6954650 bytes, checksum: fbf7280f2f781976bae6e4474c2c16c6 (MD5) / Made available in DSpace on 2016-07-20T11:52:58Z (GMT). No. of bitstreams: 1 2013_dis_lacruz.pdf: 6954650 bytes, checksum: fbf7280f2f781976bae6e4474c2c16c6 (MD5) Previous issue date: 2013 / In this dissertation we study the problem of processing k-nearest neighbours (kNN)queries in road networks considering the history of traffic conditions, in particular the case where the speed of moving objects is time-dependent. For instance, given that the user is at a given location at a certain time, the query returns the k points of interest (e.g., gas stations) that can be reached in the minimum amount of time. Previous solutions to answer kNN queries and others common queries in road networks do not work when the moving speed in each road is not constant. Building efficient and correct approaches and algorithms and storage and access schemes for processing these queries is a challenge because graph properties considered in static networks do not hold in the time dependent case. Our approach uses the well-known A∗ search algorithm by applying incremental network expansion and pruning unpromising vertices. The goal is reduce the percentage of network assessed in the search. To support the algorithm execution, we propose a storage and access method for time-dependent networks. We discuss the design and correctness of our algorithm and present experimental results that show the efficiency and effectiveness of our solution. / Nesta dissertação foi estudado o problema de processar consultas kNN em redes de rodovias considerando o histórico das condições de tráfego, em particular o caso onde a velocidade dos objetos móveis depende do tempo. Dado que um usuário está em uma dada localização e em um determinado instante de tempo, a consulta retorna os k pontos de interesse (por exemplo, postos de gasolina) que podem ser alcançados em uma quantidade de tempo mínima considerando condições históricas de tráfego. Soluções anteriores para consultas kNN e outras consultas comuns em redes de rodovia estáticas não funcionam quando o custo das arestas (tempo de viagem) é dependente do tempo. A construção de estratégias e algoritmos eficientes e corretos, e métodos de armazenamento e acesso para o processamento destas consultas é um desafio desde que algumas das propriedades de grafos comumente supostas em estratégias para redes estáticas não se mantêm para redes dependentes do tempo. O método proposto aplica uma busca A∗ à medida que vai, de maneira incremental, explorando a rede. O objetivo do método é reduzir o percentual da rede avaliado na busca. Para dar suporte à execução do algoritmo, foi também proposto um método para armazenamento e acesso para redes dependentes do tempo. A construção e a corretude do algoritmo são discutidas e são apresentados resultados experimentais com dados reais e sintéticos que mostram a eficiência da solução.

Nowadays, with the rapid development of GPS-equipped mobile devices, location-based social networks have been emerging to bridge the gap between the physical world and online social networking services. Various types of data, such as personal locations, check-ins, microblogs and social relations, have been available in location-based social networks. Efficiently managing and analyzing such data to meet users' daily query requirements become a challenging task. Among all the existing works in location-based social networks, group query is one of the most important research topics. In this thesis, we investigate query techniques for location-based services in social networking applications. Specifically, considering a location-based social network, we study spatial-aware interest group queries, geo-social {dollar}k{dollar}-cover group queries, and social-aware ridesharing group queries. Firstly, we study the spatial-aware interest group queries in location-based social networks. Recently, most of the location-based social networks release check-in services that allow users to share their visiting locations with their friends. These locations, considered as spatial objects, are usually associated with a few tags that describe the features of those locations. Utilizing such information, we propose a new type of \emph{Spatial-aware Interest Group} (SIG) query that retrieves a user group of size {dollar}k{dollar} where each user is interested in the query keywords and the users are close to each other in the Euclidean space. We prove this query problem is NP-complete, and develop two efficient algorithms IOAIR and DOAIR based on the IR-tree for the processing of SIG queries. We also validate the performance efficiency of the proposed query processing algorithms by empirical evaluation. Secondly, we study the problem of geo-social {dollar}k{dollar}-cover group queries for collaborative spatial computing. In this problem, we propose a novel type of geo-social queries, called \emph{Geo-Social K-Cover Group} (GSKCG) query, which is based on spatial containment and a new modeling of social relationships. Intuitively, given a set of spatial query points and an underlying social network, a GSKCG query finds a minimum user group in which the members satisfy certain social relationship and their associated regions can jointly cover all the query points. Albeit its practical usefulness, the GSKCG query problem is NP-complete. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution. Moreover, we design a novel index structure tailored to our problem to further accelerate query processing. Extensive experiments demonstrate that our algorithm achieves desirable performance on real-life datasets. Thirdly, we study the problem of social-aware ridesharing group queries. With the deep penetration of smartphones and geo-locating devices, ridesharing is envisioned as a promising solution to transportation-related problems such as congestion and air pollution for metropolitan cities. Despite the potential to provide significant societal and environmental benefits, ridesharing has not so far been as popular as expected. Notable barriers include the social discomfort and safety concerns when traveling with strangers. To overcome these barriers, in this thesis, we propose a new type of \emph{Social-aware Ridesharing Group} (SaRG) query which retrieves a group of riders by taking into account their social connections besides traditional spatial proximities. Because the SaRG query problem is NP-hard, we design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. We also present several incremental strategies to accelerate the search speed by reducing the repeated computations. Moreover, we propose a novel index tailored to the proposed problem to further speed up the query processing. Experimental results on real datasets show that our proposed algorithms achieve desirable performance. The works of this thesis show that the group query processing techniques are effective, which would facilitate the wider deployment of such query services in real applications

abstract: Since the advent of the internet and even more after social media platforms, the explosive growth of textual data and its availability has made analysis a tedious task. Information extraction systems are available but are generally too specific and often only extract certain kinds of information they deem necessary and extraction worthy. Using data visualization theory and fast, interactive querying methods, leaving out information might not really be necessary. This thesis explores textual data visualization techniques, intuitive querying, and a novel approach to all-purpose textual information extraction to encode large text corpus to improve human understanding of the information present in textual data. This thesis presents a modified traversal algorithm on dependency parse output of text to extract all subject predicate object pairs from text while ensuring that no information is missed out. To support full scale, all-purpose information extraction from large text corpuses, a data preprocessing pipeline is recommended to be used before the extraction is run. The output format is designed specifically to fit on a node-edge-node model and form the building blocks of a network which makes understanding of the text and querying of information from corpus quick and intuitive. It attempts to reduce reading time and enhancing understanding of the text using interactive graph and timeline. / Dissertation/Thesis / Masters Thesis Software Engineering 2018

The social community is structured around data, a lot of that data is stored in different types of databases. The meaning of this essay is to develop an interactive tool that will change the way database visualization is done, and also to research in which fields a tool of this type can be used. With the help of different methods we discovered areas where it could be of use, such as; presentation of a database, development and documentation of a database. The primary goal of the prototype was to make viewing of database structures easier, even people without prior database knowledge should be able to understand the structure. During the development we used prototypes. Their are two types of prototyping, high-fidelity and low-fidelity, both were used during the development. IDQ viewer is a prototype, which shows tables and columns in a database. One of the thoughts behind the tools functionality was that it should be platform independent, and also place independent. The tool became useful in different roles, as a learning tool, a political tool and a technical tool. As a result we can see that the usability of this tool has broadened. There exists a strong public need of tools that will generate this type of database visualization. Further research and further development of new tools are needed in the areas of database visualization.

Stored procedures are used as the current database logic for SAAB’s data model of the fighter aircraft JAS-39 Gripen electrical schemas. Since the database model was developed in 2000, a research and tests needed to be carried out to decide on whether updating the database to today's technology is applicable. Therefore, Object-Relational Mapping (ORM) is to be researched, tested and compared to stored procedures using test-driven development (TDD)concerning an important factor, that Stored procedures are well-known for, which is querying performance of the database. Moreover, how maintainability and flexibility [1] can affect decision between Stored procedures or migrating to ORM based on our subjective experience. NHibernate and Entity Framework are the two ORM solutions considered sinceSAAB uses C# in this project. The process of this project is run using scrum of the agile software development to maintain an iterative progress throughout the project timeline. In this paper, the process and methodology are covered in details and also the comparison with the test results. These results eventually lead us to the answer that ORM is not a suitable technology, and stored procedures still dominate the querying performance for SAAB’scurrent database.