Karen C. Davis

Analytics over large-scale multidimensional data: the big data revolution!

In this paper, we provide an overview of state-of-the-art research issues and achievements in the field of analytics over big data, and we extend the discussion to analytics over big multidimensional data as well, by highlighting open problems and actual research trends. Our analytical contribution is finally completed by several novel research directions arising in this field, which plays a leading role in next-generation Data Warehousing and OLAP research.

Applying multiple query optimization in mobile databases

We apply multiple query optimization to batches of pull (on-demand) requests in a mobile database system. The resulting view can answer several queries at once, and it is broadcast on a view channel dedicated to common answers of multiple queries rather than over individual downlink channels. A performance study is conducted that simulates different query workloads. The results indicate a significant savings in channel bandwidth usage and a reduction in average wait time for a multi-query approach compared to a traditional pull-based approach.

Automatic validation of object-oriented database structures

The philosophy of explicitly representing simple range classes for properties and explicitly specifying range and/or cardinality restrictions on inherited properties is adopted so that the membership of an object in classes is characterized by the objects property values. The motivation for this approach is twofold: first, it is in keeping with the database philosophy of emphasizing the representation of an application (rather than some intrinsic meaning); and second, it provides a framework for algorithmically verifying the structural aspects of the schema. An automatic classifier for the structural validation of object-oriented schemas based on sound and complete rules of inference is presented. If the classifier discovers structural relationships and/or inconsistencies, the designer can refine the schema to reflect the semantics of the application. Thus the classifier serves as an interactive design tool that can be used at any stage during the design process.<<ETX>>

Modeling data warehouse schema evolution over extended hierarchy semantics

Models for conceptual design of data warehouse schemas have been proposed, but few researchers have addressed schema evolution in a formal way and none have presented software tools for enforcing the correctness of multidimensional schema evolution operators. We generalize the core features typically found in data warehouse data models, along with modeling extended hierarchy semantics. The advanced features include multiple hierarchies, non-covering hierarchies, non-onto hierarchies, and non-strict hierarchies. We model the constructs in the Uni-level Description Language (ULD) as well as using a multilevel dictionary definition (MDD) approach. The ULD representation provides a formal foundation to specify transformation rules for the semantics of schema evolution operators. The MDD gives a basis for direct implementation in a relational database system; we define model constraints and then use the constraints to maintain integrity when schema evolution operators are applied. This paper contributes a formalism for representing data warehouse schemas and determining the validity of schema evolution operators applied to a schema. We describe a software tool that allows for visualization of the impact of schema evolution through the use of triggers and stored procedures.

A Denotational Definition of the VHDL Simulation Kernel

Abstract Increasing interest in product data exchange using ISO standard languages motivates the work described here. Standards are typically defined in natural language; this informality hinders data exchange of models described in disparate languages. Rigorous mathematical definition of the languages for data exchange should support reasoning about the correctness of transformations from one language to another. In addition, formal transformations provide the basis for provably correct automatic translation tools. A formal definition for a subset of the VHDL simulation kernel is developed here. The state corresponding to a behavioral description during simulation is formally defined, as are transformations over the state resulting from concurrent execution of processes. A contribution of this research is that it is a step toward enabling proofs that two architectural models have the same behavior.

A Denotational Approach to Object-Oriented Query Language Definition

A denotational semantics approach to object-oriented database query language definition provides an elegant formalism for specifying the syntax and semantics of the query language. The challenge is to characterize an object-oriented database (OODB) as a semantic domain, so that query expressions can be mapped to their meaning in terms of the OODB. In this research, a high-level, conceptual view of OODBs and an algebraic query language that support query processing studies are proposed. The model is applicable to structurally similar database models, which includes most object-oriented and semantic databases; it can support complex objects, inclusion dependencies (the isa relationship), exclusion dependencies (the disjoint relationship), direct associations (properties) between complex objects, multi-valued properties, and modification of inherited properties. The query algebra is a closed and complete language in the spirit of relational algebra, supporting both value-oriented and object-oriented query processing in a single language. A contribution of the denotational definition of the model and query language is the precise characterization of both the rich intension and the extension of an OODB and of the results of queries on the OODB. In this paper, the conceptual model and query algebra are discussed and formalized, followed by an illustration of how the formal definition enables proofs of algebraic transformations for logical query optimization.

Schema Extraction and Integration of Heterogeneous XML Document Collections

The availability of vast amounts of heterogeneous XML web data motivates finding efficient methods to search, integrate, query, and present this data. The structure of XML documents is useful for achieving these tasks; however, not every XML document on the web includes a schema. We discuss challenges and accomplishments in the area of generation and integration of XML schemas. We propose and implement a framework for efficient schema extraction and integration from heterogeneous XML document collections collected from the web. Our approach introduces the Schema Extended Context Free Grammar SECFG to model XML schemas, including detection of attributes, data types, and element occurrences. Unlike other implementations, our approach supports the generation of XML schemas in any XML schema language, e.g., DTDs or XSD. We compare our approach with other proposed approaches and conclude that we offer the same or better functionality more efficiently and with greater flexibility.

A Meta-Model and Semantic Mapping Methodology for Hardware Design Data Management

Database technology for storing, retrieving, and maintaining models of hardware components is necessitated by the ever-increasing complexity of designs and the design environment. This paper focuses on data representation and sharing for hardware designs specified using different standard description languages. The research offers two basic contributions: 1 a meta-model for organizing related information models is instantiated for electrical CAD modeling ECAD, with an information model for VHDL as an example, and 2 a methodology for developing semantic mappings between information models is proposed and illustrated. The information models and mappings provide a conceptual schema for database implementation and a basis for automated sharing of information through interfaces defined for heterogeneous data formats.

Foundations for object-oriented query processing

Abstract A minimal framework for an object-oriented query language standard should (1) include a formal definition of a high-level data model and the syntax and semantics of associated query languages, (2) provide the functionality of relational query languages, and (3) support proofs of correctness of transformations for logical query optimization. In this paper, a high-level conceptual model for object-oriented query processing is discussed; the model includes widely-used structural abstractions such as the isa relationship, associations (properties) between complex objects and complex objects/values, and inheritance of properties. A formal, algebraic query language for the model, inspired by relational algebra, is presented. Operators of the algebra allow queries based on values, queries that manipulate entire objects, and queries that construct new objects from existing objects/values. All queries retain connections to existing database objects, providing logical access paths to data. Each query result is a class, so the algebra has the closure property. The intensional and extensional results of query operators are summarized. Two forms of logical query optimization supported by the query algebra are outlined: algebraic transformations and classifier-based optimizations (optimizations which employ inclusion and exclusion dependencies between classes).

Benchmarking performance for migrating a relational application to a parallel implementation

Many organizations rely on relational database platforms for OLAP-style querying (aggregation and filtering) for small to medium size applications. We investigate the impact of scaling up the data sizes for such queries. We intend to illustrate what kind of performance results an organization could expect should they migrate current applications to big data environments. This paper benchmarks the performance of Hive (Thusoo et al., 2009)ź 9, a parallel data warehouse platform that is a part of the Hadoop software stack. We set up a 4-node Hadoop cluster using Hortonworks HDP 1.3.2 (Hortonworks HDP 1.3.2). We use the data generator provided by the TPC-DS benchmark (DSGen v1.1.0) to generate data of different scales. We compare the performance of loading data and querying for SQL and Hive Query Language (HiveQL) on a relational database installation (MySQL) and on a Hive cluster, respectively. We measure the speedup for query execution for three dataset sizes resulting from the scale up. Hive loads the large datasets faster than MySQL, while it is marginally slower than MySQL when loading the smaller datasets. Query execution in Hive is also faster. We also investigate executing Hive queries concurrently in workloads and conclude that serial execution of queries is a much better practice for clusters with limited resources.