A survey on big data indexing strategies

The operations of the Internet have led to a significant growth and accumulation of data known as Big Data.Individuals and organizations that utilize this data, had no idea, nor were they prepared for this data explosion.Hence, the available solutions cannot meet the needs of the growing heterogeneous data in terms of processing. This results in inefficient information retrieval or search query results.The design of indexing strategies that can support this need is required. A survey on various indexing strategies and how they are utilized for solving Big Data management issues can serve as a guide for choosing the strategy best suited for a problem, and can also serve as a base for the design of more efficient indexing strategies.The aim of the study is to explore the characteristics of the indexing strategies used in Big Data manageability by covering some of the weaknesses and strengths of B-tree, R-tree, to name but a few. This paper covers some popular indexing strategies used for Big Data management. It exposes the potentials of each by carefully exploring their properties in ways that are related to problem solving

Stochastic Modeling of Semantic Structures of Online Movie Reviews

Facing the enormous volumes of data available nowadays, we try to extract useful information from the data by properly modeling and characterizing the data. In this thesis, we focus on one particular type of semantic data --- online movie reviews, which can be found on all major movie websites. Our objective is mining movie review data to seek quantifiable patterns between reviews on the same movie, or reviews from the same reviewer. A novel approach is presented in this thesis to achieve this goal. The key idea is converting a movie review text into a list of tuples, where each tuple contains four elements: feature word, category of feature word, opinion word and polarity of opinion word. Then we further convert each tuple into an 18-dimension vector. Given a multinomial distribution representing a movie review, we can systematically and consistently quantify the similarity and dependence between reviews made by the same or different reviewers using metrics including KL distance and distance correlation, respectively. Such comparisons allow us to find reviewers sharing similarity in generated multinomial distributions, or demonstrating correlation patterns to certain extent. Among the identified pairs of frequent reviewers, we further investigate the category-wise dependency relationships between two reviewers, which are further captured by our proposed ordinary least square estimation models. The proposed data processing approaches, as well as the corresponding modeling framework, could be further leveraged to develop classification, prediction, and common randomness extraction algorithms for semantic movie review data

COSPO/CENDI Industry Day Conference

The conference's objective was to provide a forum where government information managers and industry information technology experts could have an open exchange and discuss their respective needs and compare them to the available, or soon to be available, solutions. Technical summaries and points of contact are provided for the following sessions: secure products, protocols, and encryption; information providers; electronic document management and publishing; information indexing, discovery, and retrieval (IIDR); automated language translators; IIDR - natural language capabilities; IIDR - advanced technologies; IIDR - distributed heterogeneous and large database support; and communications - speed, bandwidth, and wireless

End-to-end security in service-oriented architecture

A service-oriented architecture (SOA)-based application is composed of a number of distributed and loosely-coupled web services, which are orchestrated to accomplish a more complex functionality. Any of these web services is able to invoke other web services to offload part of its functionality. The main security challenge in SOA is that we cannot trust the participating web services in a service composition to behave as expected all the time. In addition, the chain of services involved in an end-to-end service invocation may not be visible to the clients. As a result, any violation of client’s policies could remain undetected. To address these challenges in SOA, we proposed the following contributions. First, we devised two composite trust schemes by using graph abstraction to quantitatively maintain the trust levels of different services. The composite trust values are based on feedbacks from the actual execution of services, and the structure of the SOA application. To maintain the dynamic trust, we designed the trust manager, which is a trusted-third party service. Second, we developed an end-to-end inter-service policy monitoring and enforcement framework (PME framework), which is able to dynamically inspect the interactions between services at runtime and react to the potentially malicious activities according to the client’s policies. Third, we designed an intra-service policy monitoring and enforcement framework based on taint analysis mechanism to monitor the information flow within services and prevent information disclosure incidents. Fourth, we proposed an adaptive and secure service composition engine (ASSC), which takes advantage of an efficient heuristic algorithm to generate optimal service compositions in SOA. The service compositions generated by ASSC maximize the trustworthiness of the selected services while meeting the predefined QoS constraints. Finally, we have extensively studied the correctness and performance of the proposed security measures based on a realistic SOA case study. All experimental studies validated the practicality and effectiveness of the presented solutions

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010

Beyond Question Answering: Understanding the Information Need of the User

Intelligent interaction between humans and computers has been a dream of artificial intelligence since the beginning of digital era and one of the original motivations behind the creation of artificial intelligence. A key step towards the achievement of such an ambitious goal is to enable the Question Answering systems understand the information need of the user. In this thesis, we attempt to enable the QA system's ability to understand the user's information need by three approaches. First, an clarification question generation method is proposed to help the user clarify the information need and bridge information need gap between QA system and the user. Next, a translation based model is obtained from the large archives of Community Question Answering data, to model the information need behind a question and boost the performance of question recommendation. Finally, a fine-grained classification framework is proposed to enable the systems to recommend answered questions based on information need satisfaction

Intégration holistique et entreposage automatique des données ouvertes

Statistical Open Data present useful information to feed up a decision-making system. Their integration and storage within these systems is achieved through ETL processes. It is necessary to automate these processes in order to facilitate their accessibility to non-experts. These processes have also need to face out the problems of lack of schemes and structural and sematic heterogeneity, which characterize the Open Data. To meet these issues, we propose a new ETL approach based on graphs. For the extraction, we propose automatic activities performing detection and annotations based on a model of a table. For the transformation, we propose a linear program fulfilling holistic integration of several graphs. This model supplies an optimal and a unique solution. For the loading, we propose a progressive process for the definition of the multidimensional schema and the augmentation of the integrated graph. Finally, we present a prototype and the experimental evaluations.Les statistiques présentes dans les Open Data ou données ouvertes constituent des informations utiles pour alimenter un système décisionnel. Leur intégration et leur entreposage au sein du système décisionnel se fait à travers des processus ETL. Il faut automatiser ces processus afin de faciliter leur accessibilité à des non-experts. Ces processus doivent pallier aux problèmes de manque de schémas, d'hétérogénéité structurelle et sémantique qui caractérisent les données ouvertes. Afin de répondre à ces problématiques, nous proposons une nouvelle démarche ETL basée sur les graphes. Pour l'extraction du graphe d'un tableau, nous proposons des activités de détection et d'annotation automatiques. Pour la transformation, nous proposons un programme linéaire pour résoudre le problème d'appariement holistique de données structurelles provenant de plusieurs graphes. Ce modèle fournit une solution optimale et unique. Pour le chargement, nous proposons un processus progressif pour la définition du schéma multidimensionnel et l'augmentation du graphe intégré. Enfin, nous présentons un prototype et les résultats d'expérimentations