A Nine Month Progress Report on an Investigation into Mechanisms for Improving Triple Store Performance

This report considers the requirement for fast, efficient, and scalable triple stores as part of the effort to produce the Semantic Web. It summarises relevant information in the major background field of Database Management Systems (DBMS), and provides an overview of the techniques currently in use amongst the triple store community. The report concludes that for individuals and organisations to be willing to provide large amounts of information as openly-accessible nodes on the Semantic Web, storage and querying of the data must be cheaper and faster than it is currently. Experiences from the DBMS field can be used to maximise triple store performance, and suggestions are provided for lines of investigation in areas of storage, indexing, and query optimisation. Finally, work packages are provided describing expected timetables for further study of these topics

Conceptual Modeling of Data with Provenance

Traditional database systems manage data, but often do not address its provenance. In the past, users were often implicitly familiar with data they used, how it was created (and hence how it might be appropriately used), and from which sources it came. Today, users may be physically and organizationally remote from the data they use, so this information may not be easily accessible to them. In recent years, several models have been proposed for recording provenance of data. Our work is motivated by opportunities to make provenance easy to manage and query. For example, current approaches model provenance as expressions that may be easily stored alongside data, but are difficult to parse and reconstruct for querying, and are difficult to query with available languages. We contribute a conceptual model for data and provenance, and evaluate how well it addresses these opportunities. We compare the expressive power of our model\u27s language to that of other models. We also define a benchmark suite with which to study performance of our model, and use this suite to study key model aspects implemented on existing software platforms. We discover some salient performance bottlenecks in these implementations, and suggest future work to explore improvements. Finally, we show that our implementations can comprise a logical model that faithfully supports our conceptual model

Scallop: A Language for Neurosymbolic Programming

We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation that is based on the relational data model; 2) a declarative logic programming language that is based on Datalog and supports recursion, aggregation, and negation; and 3) a framework for automatic and efficient differentiable reasoning that is based on the theory of provenance semirings. We evaluate Scallop on a suite of eight neurosymbolic applications from the literature. Our evaluation demonstrates that Scallop is capable of expressing algorithmic reasoning in diverse and challenging AI tasks, provides a succinct interface for machine learning programmers to integrate logical domain knowledge, and yields solutions that are comparable or superior to state-of-the-art models in terms of accuracy. Furthermore, Scallop's solutions outperform these models in aspects such as runtime and data efficiency, interpretability, and generalizability

Visões em bancos de dados de grafos : uma abordagem multifoco para dados heterogêneos

Orientador: Claudia Maria Bauzer MedeirosTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A pesquisa científica tornou-se cada vez mais dependente de dados. Esse novo paradigma de pesquisa demanda técnicas e tecnologias computacionais sofisticadas para apoiar tanto o ciclo de vida dos dados científicos como a colaboração entre cientistas de diferentes áreas. Uma demanda recorrente em equipes multidisciplinares é a construção de múltiplas perspectivas sobre um mesmo conjunto de dados. Soluções atuais cobrem vários aspectos, desde o projeto de padrões de interoperabilidade ao uso de sistemas de gerenciamento de bancos de dados não-relacionais. Entretanto, nenhum desses esforços atende de forma adequada a necessidade de múltiplas perspectivas, denominadas focos nesta tese. Em termos gerais, um foco é projetado e construído para atender um determinado grupo de pesquisa (mesmo no escopo de um único projeto) que necessita manipular um subconjunto de dados de interesse em múltiplos níveis de agregação/generalização. A definição e criação de um foco são tarefas complexas que demandam mecanismos capazes de manipular múltiplas representações de um mesmo fenômeno do mundo real. O objetivo desta tese é prover múltiplos focos sobre dados heterogêneos. Para atingir esse objetivo, esta pesquisa se concentrou em quatro principais problemas. Os problemas inicialmente abordados foram: (1) escolher um paradigma de gerenciamento de dados adequado e (2) elencar os principais requisitos de pesquisas multifoco. Nossos resultados nos direcionaram para a adoção de bancos de dados de grafos como solução para o problema (1) e a utilização do conceito de visões, de bancos de dados relacionais, para o problema (2). Entretanto, não há consenso sobre um modelo de dados para bancos de dados de grafos e o conceito de visões é pouco explorado nesse contexto. Com isso, os demais problemas tratados por esta pesquisa são: (3) a especificação de um modelo de dados de grafos e (4) a definição de um framework para manipular visões em bancos de dados de grafos. Nossa pesquisa nesses quatro problemas resultaram nas contribuições principais desta tese: (i) apontar o uso de bancos de dados de grafos como camada de persistência em pesquisas multifoco - um tipo de banco de dados de esquema flexível e orientado a relacionamentos que provê uma ampla compreensão sobre as relações entre os dados; (ii) definir visões para bancos de dados de grafos como mecanismo para manipular múltiplos focos, considerando operações de manipulação de dados em grafos, travessias e algoritmos de grafos; (iii) propor um modelo de dados para grafos - baseado em grafos de propriedade - para lidar com a ausência de um modelo de dados pleno para grafos; (iv) especificar e implementar um framework, denominado Graph-Kaleidoscope, para prover o uso de visões em bancos de dados de grafos e (v) validar nosso framework com dados reais em aplicações distintas - em biodiversidade e em recursos naturais - dois típicos exemplos de pesquisas multidisciplinares que envolvem a análise de interações de fenômenos a partir de dados heterogêneosAbstract: Scientific research has become data-intensive and data-dependent. This new research paradigm requires sophisticated computer science techniques and technologies to support the life cycle of scientific data and collaboration among scientists from distinct areas. A major requirement is that researchers working in data-intensive interdisciplinary teams demand construction of multiple perspectives of the world, built over the same datasets. Present solutions cover a wide range of aspects, from the design of interoperability standards to the use of non-relational database management systems. None of these efforts, however, adequately meet the needs of multiple perspectives, which are called foci in the thesis. Basically, a focus is designed/built to cater to a research group (even within a single project) that needs to deal with a subset of data of interest, under multiple ggregation/generalization levels. The definition and creation of a focus are complex tasks that require mechanisms and engines to manipulate multiple representations of the same real world phenomenon. This PhD research aims to provide multiple foci over heterogeneous data. To meet this challenge, we deal with four research problems. The first two were (1) choosing an appropriate data management paradigm; and (2) eliciting multifocus requirements. Our work towards solving these problems made as choose graph databases to answer (1) and the concept of views in relational databases for (2). However, there is no consensual data model for graph databases and views are seldom discussed in this context. Thus, research problems (3) and (4) are: (3) specifying an adequate graph data model and (4) defining a framework to handle views on graph databases. Our research in these problems results in the main contributions of this thesis: (i) to present the case for the use of graph databases in multifocus research as persistence layer - a schemaless and relationship driven type of database that provides a full understanding of data connections; (ii) to define views for graph databases to support the need for multiple foci, considering graph data manipulation, graph algorithms and traversal tasks; (iii) to propose a property graph data model (PGDM) to fill the gap of absence of a full-fledged data model for graphs; (iv) to specify and implement a framework, named Graph-Kaleidoscope, that supports views over graph databases and (v) to validate our framework for real world applications in two domains - biodiversity and environmental resources - typical examples of multidisciplinary research that involve the analysis of interactions of phenomena using heterogeneous dataDoutoradoCiência da ComputaçãoDoutora em Ciência da Computaçã

Local Radiance

Recent years have seen a proliferation of web applications based on content management systems (CMS). Using a CMS, non-technical content authors are able to define custom content types to support their needs. These content type names and the attribute names in each content type are typically domain-specific and meaningful to the content authors. The ability of a CMS to support a multitude of content types allows for endless creation and customization but also leads to a large amount of heterogeneity within a single application. While this meaningful heterogeneity is beneficial, it introduces the problem of how to write reusable functionality (e.g., general purpose widgets) that can work across all the different types. Traditional information integration can solve the problem of schema heterogeneity by defining a single global schema that captures the shared semantics of the heterogeneous (local) schemas. Functionality and queries can then be written against the global schema and return data from local sources in the form of the global schema, but the meaningful local semantics (such as type and attribute names) are not returned. Mappings are also complex and require skilled developers to create. Here we propose a system that we call \textit{local radiance} (LR) that captures both global shared semantics as well as local, beneficial heterogeneity. We provide a formal definition of our system that includes domain structures---small, global schema fragments that represent shared domain-specific semantics--- and canonical structures---domain-independent global schema fragments used to build generic global widgets. We define mappings between local, domain, and canonical levels. Our query language extends the relational algebra to support queries that radiate local semantics to the domain and canonical levels as well as inserting and updating heterogeneous local data from generic global widgets. We characterize the expressive power of our mapping language and show how it can be used to perform complex data and metadata transformations. Through a user study, we evaluate the ability of non-technical users to perform mapping tasks and find that it is both understandable and usable. We report on the ongoing development (in CMSs and a relational database) of LR systems, demonstrate how widgets can be built using local radiance, and show how LR is being used in a number of online public educational repositories

Using domain ontologies to help track data provenance.

Motivating example. POESIA ontologies and ontological coverages. Ontological estimation of data provenance. Ontological nets for data integration. Data integration operators. Data reconciling through articulation of ontologies. Semantic workflows. Related work. Conclusions

Physical Plan Instrumentation in Databases: Mechanisms and Applications

Database management systems (DBMSs) are designed with the goal set to compile SQL queries to physical plans that, when executed, provide results to the SQL queries. Building on this functionality, an ever-increasing number of application domains (e.g., provenance management, online query optimization, physical database design, interactive data profiling, monitoring, and interactive data visualization) seek to operate on how queries are executed by the DBMS for a wide variety of purposes ranging from debugging and data explanation to optimization and monitoring. Unfortunately, DBMSs provide little, if any, support to facilitate the development of this class of important application domains. The effect is such that database application developers and database system architects either rewrite the database internals in ad-hoc ways; work around the SQL interface, if possible, with inevitable performance penalties; or even build new databases from scratch only to express and optimize their domain-specific application logic over how queries are executed. To address this problem in a principled manner in this dissertation, we introduce a prototype DBMS, namely, Smoke, that exposes instrumentation mechanisms in the form of a framework to allow external applications to manipulate physical plans. Intuitively, a physical plan is the underlying representation that DBMSs use to encode how a SQL query will be executed, and providing instrumentation mechanisms at this representation level allows applications to express and optimize their logic on how queries are executed. Having such an instrumentation-enabled DBMS in-place, we then consider how to express and optimize applications that rely their logic on how queries are executed. To best demonstrate the expressive and optimization power of instrumentation-enabled DBMSs, we express and optimize applications across several important domains including provenance management, interactive data visualization, interactive data profiling, physical database design, online query optimization, and query discovery. Expressivity-wise, we show that Smoke can express known techniques, introduce novel semantics on known techniques, and introduce new techniques across domains. Performance-wise, we show case-by-case that Smoke is on par with or up-to several orders of magnitudes faster than state-of-the-art imperative and declarative implementations of important applications across domains. As such, we believe our contributions provide evidence and form the basis towards a class of instrumentation-enabled DBMSs with the goal set to express and optimize applications across important domains with core logic over how queries are executed by DBMSs

Direct Manipulation Querying of Database Systems.

Database systems are tremendously powerful and useful, as evidenced by their popularity in modern business. Unfortunately, for non-expert users, to use a database is still a daunting task due to its poor usability. This PhD dissertation examines stages in the information seeking process and proposes techniques to help users interact with the database through direct manipulation, which has been proven a natural interaction paradigm. For the first stage of information seeking, query formulation, we proposed a spreadsheet algebra upon which a direct manipulation interface for database querying can be built. We developed a spreadsheet algebra that is powerful (capable of expressing at least all single-block SQL queries) and can be intuitively implemented in a spreadsheet. In addition, we proposed assisted querying by browsing, where we help users query the database through browsing. For the second stage, result review, instead of asking users to review possibly many results in a flat table, we proposed a hierarchical navigation scheme that allows users to browse the results through representatives with easy drill-down and filtering capabilities. We proposed an efficient tree-based method for generating the representatives. For the query refinement stage, we proposed and implemented a provenance-based automatic refinement framework. Users label a set of output tuples and our framework produces a ranked list of changes that best improve the query. This dissertation significantly lowers the barrier for non-expert users and reduces the effort for expert users to use a database.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86282/1/binliu_1.pd