Functional dependencies for XML : axiomatisation and normal form in the presence of frequencies and identifiers : a thesis presented in partial fulfilment of the requirements for the degree of Master of Sciences in Information Sciences at Massey University, Palmerston North, New Zealand

XML has gained popularity as a markup language for publishing and exchanging data on the web. Nowadays, there are also ongoing interests in using XML for representing and actually storing data. In particular, much effort has been directed towards turning XML into a real data model by improving the semantics that can be expressed about XML documents. Various works have addressed how to define different classes of integrity constraints and the development of a normalisation theory for XML. One area which received little to no attention from the research community up to five years ago is the study of functional dependencies in the context of XML [37]. Since then, there has been increasingly more research investigating functional dependencies in XML. Nevertheless, a comprehensive dependency theory and normalisation theory for XML have yet to emerge. Functional dependencies are an integral part of database theory in the relational data model (RDM). In particular, functional dependencies have been vital in the investigation of how to design "good" relational database schemas which avoid or minimise problems relating to data redundancy and data inconsistency. Since the same problems can be shown to exist in poorly designed XML schemas 1 , there is a need to investigate how these problems can be eliminated in the context of XML. We believe that the study of an analogy to relational functional dependencies in the context of XML is equally significant towards designing "good" XML schemas. [FROM INTRODUCTION

Characterization of order-like dependencies with formal concept analysis

Functional Dependencies (FDs) play a key role in many fields of the relational database model, one of the most widely used database systems. FDs have also been applied in data analysis, data quality, knowl- edge discovery and the like, but in a very limited scope, because of their fixed semantics. To overcome this limitation, many generalizations have been defined to relax the crisp definition of FDs. FDs and a few of their generalizations have been characterized with Formal Concept Analysis which reveals itself to be an interesting unified framework for charac- terizing dependencies, that is, understanding and computing them in a formal way. In this paper, we extend this work by taking into account order-like dependencies. Such dependencies, well defined in the database field, consider an ordering on the domain of each attribute, and not sim- ply an equality relation as with standard FDs.Peer ReviewedPostprint (published version

Strong types for relational databases: functional pearl

Haskell's type system with multi-parameter constructor classes and functional dependencies allows static (compile-time) computations to be expressed by logic programming on the level of types. This emergent capability has been exploited for instance to model arbitrary-length tuples (heterogeneous lists), extensible records, functions with variable length argument lists, and (homogenous) lists of statically fixed length (vectors).We explain how type-level programming can be exploited to define a strongly-typed model of relational databases and operations on them. In particular, we present a strongly typed embedding of a significant subset of SQL in Haskell. In this model, meta-data is represented by type-level entities that guard the semantic correctness of database operations at compile time.Apart from the standard relational database operations, such as selection and join, we model functional dependencies (among table attributes), normal forms, and operations for database transformation. We show how functional dependency information can be represented at the type level, and can be transported through operations. This means that type inference statically computes functional dependencies on the result from those on the arguments.Our model shows that Haskell can be used to design and prototype typed languages for designing, programming, and transforming relational databasesFundação para a Ciência e a Tecnologia (FCT) - POSI/ICHS/44304/2002; SFRH/BPD/11609/2002

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

characterization of order-like dependencies with formal concept analysis

Functional Dependencies (FDs) play a key role in many fields of the relational database model, one of the most widely used database systems. FDs have also been applied in data analysis, data quality, knowledge discovery and the like, but in a very limited scope, because of their fixed semantics. To overcome this limitation, many generalizations have been defined to relax the crisp definition of FDs. FDs and a few of their generalizations have been characterized with Formal Concept Analysis which reveals itself to be an interesting unified framework for characterizing dependencies, that is, understanding and computing them in a formal way. In this paper, we extend this work by taking into account order-like dependencies. Such dependencies, well defined in the database field, consider an ordering on the domain of each attribute, and not simply an equality relation as with standard FDsPostprint (published version