Querying heterogeneous data in NoSQL document stores

La problématique de cette thèse porte sur l'interrogation de données hétérogènes dans les systèmes de stockage "not-only SQL" (noSQL) orientés documents. Ces derniers ont connu un important développement ces dernières années en raison de leur capacité à gérer de manière flexible et efficace d'importantes masses de documents. Ils reposent sur le principe "schema-less" consistant à ne plus considérer un schéma unique pour un ensemble de données, appelé collection de documents. Cette flexibilité dans la structuration des données complexifie l'interrogation pour les utilisateurs qui doivent connaître l'ensemble des différents schémas des données manipulées lors de l'écriture de requêtes. Les travaux développés dans cette thèse sont menés dans le cadre du projet neoCampus. Ils se focalisent sur l'interrogation de documents structurellement hétérogènes, en particulier sur le problème de schémas variables. Nous proposons la construction d'un dictionnaire de données qui permet de retrouver tous les schémas des documents. Chaque clef, entrée du dictionnaire, correspond à un chemin absolu ou partiel existant dans au moins un document de la collection. Cette clef est associée aux différents chemins absolus correspondants dans l'ensemble de la collection de documents. Le dictionnaire est alors exploité pour réécrire de manière automatique et transparente les requêtes des utilisateurs. Les requêtes utilisateurs sont établies sur la base des clés du dictionnaire (chemins partiels ou absolus) et sont automatiquement réécrites en exploitant le dictionnaire afin de prendre en compte l'ensemble des chemins absolus existants dans les documents de la collection. Dans cette thèse, nous menons une étude de l'état de l'art des travaux s'attachant à résoudre l'interrogation de documents structurellement hétérogènes, et nous en proposons une classification. Ensuite, nous comparons ces travaux en fonction de critères qui permettent de positionner et différencier notre contribution. Nous définissions formellement les concepts classiques liés aux systèmes orientés documents (document, collection, etc), puis nous étendons cette formalisation par des concepts supplémentaires : chemins absolus et partiels, schémas de document, dictionnaire. Pour la manipulation et l'interrogation des documents, nous définissons un noyau algébrique minimal fermé composé de cinq opérateurs : sélection, projection, des-imbrication (unnest), agrégation et jointure (left-join). Nous définissons chaque opérateur et expliquons son évaluation par un moteur de requête classique. Ensuite, nous établissons la réécriture de chacun des opérateurs à partir du dictionnaire. Nous définissons le processus de réécriture des requêtes utilisateurs qui produit une requête évaluable par un moteur de requête classique en conservant la logique des opérateurs classiques (chemins inexistants, valeurs nulles). Nous montrons comment la réécriture d'une requête initialement construite avec des chemins partiels et/ou absolus permet de résoudre le problème d'hétérogénéité structurelle des documents. Enfin, nous menons des expérimentations afin de valider les concepts formels que nous introduisons tout au long de cette thèse. Nous évaluons la construction et la maintenance du dictionnaire en changeant la configuration en termes de nombre de structures par collection étudiée et de taille de collection. Puis, nous évaluons le moteur de réécriture de requêtes en le comparant à une évaluation de requête dans un contexte sans hétérogénéité structurelle puis dans un contexte de multi-requêtes. Toutes nos expérimentations ont été menées sur des collection synthétiques avec plusieurs niveaux d'imbrications, différents nombres de structure par collection, et différentes tailles de collections. Récemment, nous avons intégré notre contribution dans le projet neOCampus afin de gérer l'hétérogénéité lors de l'interrogation des données de capteurs implantés dans le campus de l'université Toulouse III-Paul Sabatier.This thesis discusses the problems related to querying heterogeneous data in document-oriented systems. Document-oriented "not-only SQL" (noSQL) storage systems have undergone significant development in recent years due to their ability to manage large amounts of documents in a flexible and efficient manner. These systems rely on the "schema-less" concept where no there is no requirement to consider a single schema for a set of data, called a collection of documents. This flexibility in data structures makes the query formulation more complex and users need to know all the different schemas of the data manipulated during the query formulation. The work developed in this thesis subscribes into the frame of neOCampus project. It focuses on issues in the manipulation and the querying of structurally heterogeneous document collections, mainly the problem of variable schemas. We propose the construction of a dictionary of data that makes it possible to find all the schemas of the documents. Each key, a dictionary entry, corresponds to an absolute or partial path existing in at least one document of the collection. This key is associated to all the corresponding absolute paths throughout the collection of heterogeneous documents. The dictionary is then exploited to automatically and transparently reformulate queries from users. The user queries are formulated using the dictionary keys (partial or absolute paths) and are automatically reformulated using the dictionary to consider all the existing paths in all documents in the collection. In this thesis, we conduct a state-of-the-art survey of the work related to solving the problem of querying data of heterogeneous structures, and we propose a classification. Then, we compare these works according to criteria that make it possible to position our contribution. We formally define the classical concepts related to document-oriented systems (document, collection, etc). Then, we extend this formalisation with additional concepts: absolute and partial paths, document schemas, dictionary. For manipulating and querying heterogeneous documents, we define a closed minimal algebraic kernel composed of five operators: selection, projection, unnest, aggregation and join (left join). We define each operator and explain its classical evaluation by the native document querying engine. Then we establish the reformulation rules of each of these operators based on the use of the dictionary. We define the process of reformulating user queries that produces a query that can be evaluated by most document querying engines while keeping the logic of the classical operators (misleading paths, null values). We show how the reformulation of a query initially constructed with partial and/or absolute paths makes it possible to solve the problem of structural heterogeneity of documents. Finally, we conduct experiments to validate the formal concepts that we introduce throughout this thesis. We evaluate the construction and maintenance of the dictionary by changing the configuration in terms of number of structures per collection studied and collection size. Then, we evaluate the query reformulation engine by comparing it to a query evaluation in a context without structural heterogeneity and then in a context of executing multiple queries. All our experiments were conducted on synthetic collections with several levels of nesting, different numbers of structures per collection, and on varying collection sizes. Recently, we deployed our contributions in the neOCampus project to query heterogeneous sensors data installed at different classrooms and the library at the campus of the university of Toulouse III-Paul Sabatier

Acquiring symbolic design optimization problem reformulation knowledge: On computable relationships between design syntax and semantics

This thesis presents a computational method for the inductive inference of explicit and implicit semantic design knowledge from the symbolic-mathematical syntax of design formulations using an unsupervised pattern recognition and extraction approach. Existing research shows that AI / machine learning based design computation approaches either require high levels of knowledge engineering or large training databases to acquire problem reformulation knowledge. The method presented in this thesis addresses these methodological limitations. The thesis develops, tests, and evaluates ways in which the method may be employed for design problem reformulation. The method is based on the linear algebra based factorization method Singular Value Decomposition (SVD), dimensionality reduction and similarity measurement through unsupervised clustering. The method calculates linear approximations of the associative patterns of symbol cooccurrences in a design problem representation to infer induced coupling strengths between variables, constraints and system components. Unsupervised clustering of these approximations is used to identify useful reformulations. These two components of the method automate a range of reformulation tasks that have traditionally required different solution algorithms. Example reformulation tasks that it performs include selection of linked design variables, parameters and constraints, design decomposition, modularity and integrative systems analysis, heuristically aiding design “case” identification, topology modeling and layout planning. The relationship between the syntax of design representation and the encoded semantic meaning is an open design theory research question. Based on the results of the method, the thesis presents a set of theoretical postulates on computable relationships between design syntax and semantics. The postulates relate the performance of the method with empirical findings and theoretical insights provided by cognitive neuroscience and cognitive science on how the human mind engages in symbol processing and the resulting capacities inherent in symbolic representational systems to encode “meaning”. The performance of the method suggests that semantic “meaning” is a higher order, global phenomenon that lies distributed in the design representation in explicit and implicit ways. A one-to-one local mapping between a design symbol and its meaning, a largely prevalent approach adopted by many AI and learning algorithms, may not be sufficient to capture and represent this meaning. By changing the theoretical standpoint on how a “symbol” is defined in design representations, it was possible to use a simple set of mathematical ideas to perform unsupervised inductive inference of knowledge in a knowledge-lean and training-lean manner, for a knowledge domain that traditionally relies on “giving” the system complex design domain and task knowledge for performing the same set of tasks

A rapid prototyping/artificial intelligence approach to space station-era information management and access

Applications of rapid prototyping and Artificial Intelligence techniques to problems associated with Space Station-era information management systems are described. In particular, the work is centered on issues related to: (1) intelligent man-machine interfaces applied to scientific data user support, and (2) the requirement that intelligent information management systems (IIMS) be able to efficiently process metadata updates concerning types of data handled. The advanced IIMS represents functional capabilities driven almost entirely by the needs of potential users. Space Station-era scientific data projected to be generated is likely to be significantly greater than data currently processed and analyzed. Information about scientific data must be presented clearly, concisely, and with support features to allow users at all levels of expertise efficient and cost-effective data access. Additionally, mechanisms for allowing more efficient IIMS metadata update processes must be addressed. The work reported covers the following IIMS design aspects: IIMS data and metadata modeling, including the automatic updating of IIMS-contained metadata, IIMS user-system interface considerations, including significant problems associated with remote access, user profiles, and on-line tutorial capabilities, and development of an IIMS query and browse facility, including the capability to deal with spatial information. A working prototype has been developed and is being enhanced

A logic programming framework for modeling temporal objects

Published versio

Web and Semantic Web Query Languages

A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion

The use of ontologies for effective knowledge modelling and information retrieval

© 2017 The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex databases but also the semantic relationships between data stored in databases. In order to overcome such difficulties, researchers have been focusing on knowledge representation and interactive query generation through ontologies, with particular emphasis on improving the interface between data and search requests in order to bring the result sets closer to users research requirements. This paper discusses ontology-based information retrieval approaches and techniques by taking into consideration the aspects of ontology modelling, processing and the translation of ontological knowledge into database search requests. It also extensively compares the existing ontology-to-database transformation and mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. The research outcomes, recommendations and future challenges presented in this paper can bridge the gap between ontology and relational models to generate precise search requests using ontologies. Moreover, the comparison presented between various ontology-based information retrieval, database-to-ontology transformations and ontology-to-database mappings approaches provides a reference for enhancing the searching capabilities of massively loaded information management systems

Flexible Data Refreshing Architecture for Health Information System Integration

Background: Having a consistent and unified view of heterogeneous distributed medical information sources is an inevitable need of health informatics. Integrating medical information of patients or about a disease, a treatment or side effects of a drug, etc, is very useful to help medical education, to achieve medical research goals and to provide the computer- based decision support systems. Contribution: This article proposes a flexible incremental update method for the materialized part of the integration system. It permits us to manage the integration system according to the characteristics of the data sources which can change. Method: This paper presents a hybrid data integration approach in which the materialized part of the system in mediator is the object indexation structure based on an instance classification of the sources objects which correspond to the global schema. The object identifier of each object in the indexation structure is materialized together with the attributes which are needed for the incremental updating of this indexation (classifying attributes). Results: The main idea of this paper is to develop a hybrid data integration framework, which represents a new aspect of a hybrid method focusing on flexible data refreshing. Conclusion: This hybrid approach implements a vertical hybrid approach. It means that at the mediator level, some data of each object are materialized and others are virtual

Formal concept matching and reinforcement learning in adaptive information retrieval

The superiority of the human brain in information retrieval (IR) tasks seems to come firstly from its ability to read and understand the concepts, ideas or meanings central to documents, in order to reason out the usefulness of documents to information needs, and secondly from its ability to learn from experience and be adaptive to the environment. In this work we attempt to incorporate these properties into the development of an IR model to improve document retrieval. We investigate the applicability of concept lattices, which are based on the theory of Formal Concept Analysis (FCA), to the representation of documents. This allows the use of more elegant representation units, as opposed to keywords, in order to better capture concepts/ideas expressed in natural language text. We also investigate the use of a reinforcement leaming strategy to learn and improve document representations, based on the information present in query statements and user relevance feedback. Features or concepts of each document/query, formulated using FCA, are weighted separately with respect to the documents they are in, and organised into separate concept lattices according to a subsumption relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the concepts in the lattice representation. This avoids implementation drawbacks faced by other FCA-based approaches. Retrieval of a document for an information need is based on concept matching between concept lattice representations of a document and a query. The learning strategy works by making the similarity of relevant documents stronger and non-relevant documents weaker for each query, depending on the relevance judgements of the users on retrieved documents. Our approach is radically different to existing FCA-based approaches in the following respects: concept formulation; weight assignment to object-attribute pairs; the representation of each document in a separate concept lattice; and encoding concept lattices in BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our learning strategy makes use of relevance feedback information to enhance document representations, thus making the document representations dynamic and adaptive to the user interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are presented and compared with published results. In particular, the performance of the system is shown to improve significantly as the system learns from experience.The School of Computing, University of Plymouth, UK