Probabilistic Relational Model Benchmark Generation

The validation of any database mining methodology goes through an evaluation process where benchmarks availability is essential. In this paper, we aim to randomly generate relational database benchmarks that allow to check probabilistic dependencies among the attributes. We are particularly interested in Probabilistic Relational Models (PRMs), which extend Bayesian Networks (BNs) to a relational data mining context and enable effective and robust reasoning over relational data. Even though a panoply of works have focused, separately , on the generation of random Bayesian networks and relational databases, no work has been identified for PRMs on that track. This paper provides an algorithmic approach for generating random PRMs from scratch to fill this gap. The proposed method allows to generate PRMs as well as synthetic relational data from a randomly generated relational schema and a random set of probabilistic dependencies. This can be of interest not only for machine learning researchers to evaluate their proposals in a common framework, but also for databases designers to evaluate the effectiveness of the components of a database management system

Representing relational database designs in the UML.

Database Reverse Engineering (DBRE) refers to mapping relational schemas into semantically enriched models. Most of researches on DBRE map relational databases into the ER or EER models. They differ in input requirements and output. Unlike previous DBRE approaches, this thesis provides a methodology for mapping relational databases into an object-oriented designs in the UML. A distinct advantage of our approach is that it unifies objected oriented application designs and relational database designs. In this thesis, we first provide the Unified Modeling Language (UML) representations for basic relation schemas, with rigorous definitions for relational model concepts given by the UML metamodel and Object Constraint Language (OCL). Then, the paper classifies relational databases based on the types and number of inclusion dependencies. Finally, a general approach for mapping relational databases into object-oriented designs in the UML is specified.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .Z53. Source: Masters Abstracts International, Volume: 40-06, page: 1562. Adviser: Liwu Li. Thesis (M.Sc.)--University of Windsor (Canada), 2001

Learning Models over Relational Data using Sparse Tensors and Functional Dependencies

Integrated solutions for analytics over relational databases are of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations; export the training dataset defined by such queries; convert this dataset into the format of an external learning tool; and train the desired model using this tool. These integrated solutions are also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This article introduces a unified framework for training and evaluating a class of statistical learning models over relational databases. This class includes ridge linear regression, polynomial regression, factorization machines, and principal component analysis. We show that, by synergizing key tools from database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as tensor and matrix operations, one can formulate relational analytics problems and design efficient (query and data) structure-aware algorithms to solve them. This theoretical development informed the design and implementation of the AC/DC system for structure-aware learning. We benchmark the performance of AC/DC against R, MADlib, libFM, and TensorFlow. For typical retail forecasting and advertisement planning applications, AC/DC can learn polynomial regression models and factorization machines with at least the same accuracy as its competitors and up to three orders of magnitude faster than its competitors whenever they do not run out of memory, exceed 24-hour timeout, or encounter internal design limitations.Comment: 61 pages, 9 figures, 2 table

A Call to Arms: Revisiting Database Design

Good database design is crucial to obtain a sound, consistent database, and - in turn - good database design methodologies are the best way to achieve the right design. These methodologies are taught to most Computer Science undergraduates, as part of any Introduction to Database class. They can be considered part of the "canon", and indeed, the overall approach to database design has been unchanged for years. Moreover, none of the major database research assessments identify database design as a strategic research direction. Should we conclude that database design is a solved problem? Our thesis is that database design remains a critical unsolved problem. Hence, it should be the subject of more research. Our starting point is the observation that traditional database design is not used in practice - and if it were used it would result in designs that are not well adapted to current environments. In short, database design has failed to keep up with the times. In this paper, we put forth arguments to support our viewpoint, analyze the root causes of this situation and suggest some avenues of research.Comment: Removed spurious column break. Nothing else was change

Theoretical framework of temporal databases.

by Lam Wing Hee.Thesis (M.Phil.)--Chinese University of Hong Kong, 1991.Bibliography: leaves [56]-59.List of Figures --- p.vAcknowledgements --- p.viChapter 1. --- Introduction --- p.1Chapter 1.1 --- Historical Data and Temporal Databases --- p.1Chapter 1.2 --- Valid Time and Transaction Time --- p.3Chapter 1.2.1 --- Snapshot Databases --- p.3Chapter 1.2.2 --- Rollback Databases --- p.4Chapter 1.2.3 --- Historical Databases --- p.6Chapter 1.2.4 --- Temporal Databases --- p.7Chapter 1.3 --- Literature Review --- p.8Chapter 1.3.1 --- Data Models --- p.9Chapter 1.3.2 --- Query Languages --- p.11Chapter 1.3.3 --- Logical Design --- p.13Chapter 2. --- The Temporal Relational Data Model --- p.14Chapter 2.1 --- The Temporal Relational Data Model - Informal Description --- p.14Chapter 2.2 --- The Temporal Relational Data Model - Formal Description --- p.15Chapter 2.2.1 --- Valid and Transaction Time Intervals --- p.16Chapter 2.2.2 --- "Attributes, Tuples and Temporal Relations" --- p.16Chapter 2.3 --- What is a Key in Temporal Relations? --- p.17Chapter 3. --- The Temporal Relational Algebra --- p.20Chapter 3.1 --- Operations in the Temporal Relational Algebra --- p.20Chapter 3.1.1 --- Union and Set Difference --- p.21Chapter 3.1.2 --- Selection --- p.21Chapter 3.1.3 --- Projection --- p.23Chapter 3.1.4 --- Join --- p.24Chapter 3.1.4.1 --- Natural Join --- p.25Chapter 3.2 --- Temporal Relational Algebra and TempSQL --- p.30Chapter 4. --- Classical Data Dependencies in Temporal Relations --- p.32Chapter 4.1 --- Functional Dependency in the Temporal Relational Model --- p.32Chapter 4.2 --- Multivalued Dependency in the Temporal Relational Model --- p.33Chapter 4.3 --- Relationship with Snapshot Data Dependencies --- p.34Chapter 4.4 --- Lossless Decomposition --- p.35Chapter 5. --- Asynchronous Dependency --- p.39Chapter 5.1 --- Asynchronous Dependency --- p.40Chapter 5.2 --- Asynchronous Normal Form --- p.41Chapter 5.3 --- Generalized Form of Data Dependency --- p.42Chapter 5.3.1 --- Embedded Implicational Dependency --- p.43Chapter 5.3.2 --- Algebraic Dependency --- p.45Chapter 5.4 --- Asynchronous Dependency versus Synchronous Dependency --- p.46Chapter 6. --- Conclusions --- p.48Chapter 6.1 --- Summary of the Thesis --- p.48Chapter 6.2 --- Unsolved Problems and Research Directions --- p.49Chapter 6.2.1 --- Equivalent Representations in the Temporal Relational Model --- p.49Chapter 6.2.2 --- The Notion of 'Completeness' of Temporal Query Languages --- p.50Chapter 6.2.3 --- Logical Basis for Temporal Data Models and Languages --- p.51Chapter 6.2.4 --- Other Temporal Dependencies --- p.51Chapter 6.2.5 --- Research Directions in Topics other than Theory --- p.52Appendix Proofs of Theorems --- p.53Bibliography --- p.5

XML document design via GN-DTD

Designing a well-structured XML document is important for the sake of readability and maintainability. More importantly, this will avoid data redundancies and update anomalies when maintaining a large quantity of XML based documents. In this paper, we propose a method to improve XML structural design by adopting graphical notations for Document Type Definitions (GN-DTD), which is used to describe the structure of an XML document at the schema level. Multiples levels of normal forms for GN-DTD are proposed on the basis of conceptual model approaches and theories of normalization. The normalization rules are applied to transform a poorly designed XML document into a well-designed based on normalized GN-DTD, which is illustrated through examples