Query processing in temporal object-oriented databases

This PhD thesis is concerned with historical data management in the context of objectoriented databases. An extensible approach has been explored to processing temporal object queries within a uniform query framework. By the uniform framework, we mean temporal queries can be processed within the existing object-oriented framework that is extended from relational framework, by extending the existing query processing techniques and strategies developed for OODBs and RDBs. The unified model of OODBs and RDBs in UmSQL/X has been adopted as a basis for this purpose. A temporal object data model is thereby defined by incorporating a time dimension into this unified model of OODBs and RDBs to form temporal relational-like cubes but with the addition of aggregation and inheritance hierarchies. A query algebra, that accesses objects through these associations of aggregation, inheritance and timereference, is then defined as a general query model /language. Due to the extensive features of our data model and reducibility of the algebra, a layered structure of query processor is presented that provides a uniforrn framework for processing temporal object queries. Within the uniform framework, query transformation is carried out based on a set of transformation rules identified that includes the known relational and object rules plus those pertaining to the time dimension. To evaluate a temporal query involving a path with timereference, a strategy of decomposition is proposed. That is, evaluation of an enhanced path, which is defined to extend a path with time-reference, is decomposed by initially dividing the path into two sub-paths: one containing the time-stamped class that can be optimized by making use of the ordering information of temporal data and another an ordinary sub-path (without time-stamped classes) which can be further decomposed and evaluated using different algorithms. The intermediate results of traversing the two sub-paths are then joined together to create the query output. Algorithms for processing the decomposed query components, i. e., time-related operation algorithms, four join algorithms (nested-loop forward join, sort-merge forward join, nested-loop reverse join and sort-merge reverse join) and their modifications, have been presented with cost analysis and implemented with stream processing techniques using C++. Simulation results are also provided. Both cost analysis and simulation show the effects of time on the query processing algorithms: the join time cost is linearly increased with the expansion in the number of time-epochs (time-dimension in the case of a regular TS). It is also shown that using heuristics that make use of time information can lead to a significant time cost saving. Query processing with incomplete temporal data has also been discussed

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

PFTijah: text search in an XML database system

This paper introduces the PFTijah system, a text search system that is integrated with an XML/XQuery database management system. We present examples of its use, we explain some of the system internals, and discuss plans for future work. PFTijah is part of the open source release of MonetDB/XQuery

Is a Dataframe Just a Table?

Querying data is core to databases and data science. However, the two communities have seemingly different concepts and use cases. As a result, both designers and users of the query languages disagree on whether the core abstractions - dataframes (data science) and tables (databases) - and the operations are the same. To investigate the difference from a PL-HCI perspective, we identify the basic affordances provided by tables and dataframes and how programming experiences over tables and dataframes differ. We show that the data structures nudge programmers to query and store their data in different ways. We hope the case study could clarify confusions, dispel misinformation, increase cross-pollination between the two communities, and identify open PL-HCI questions

Temporal Stream Algebra

Data stream management systems (DSMS) so far focus on event queries and hardly consider combined queries to both data from event streams and from a database. However, applications like emergency management require combined data stream and database queries. Further requirements are the simultaneous use of multiple timestamps after different time lines and semantics, expressive temporal relations between multiple time-stamps and exible negation, grouping and aggregation which can be controlled, i. e. started and stopped, by events and are not limited to fixed-size time windows. Current DSMS hardly address these requirements. This article proposes Temporal Stream Algebra (TSA) so as to meet the afore mentioned requirements. Temporal streams are a common abstraction of data streams and data- base relations; the operators of TSA are generalizations of the usual operators of Relational Algebra. A in-depth 'analysis of temporal relations guarantees that valid TSA expressions are non-blocking, i. e. can be evaluated incrementally. In this respect TSA differs significantly from previous algebraic approaches which use specialized operators to prevent blocking expressions on a "syntactical" level

QB2OLAP : enabling OLAP on statistical linked open data

Publication and sharing of multidimensional (MD) data on the Semantic Web (SW) opens new opportunities for the use of On-Line Analytical Processing (OLAP). The RDF Data Cube (QB) vocabulary, the current standard for statistical data publishing, however, lacks key MD concepts such as dimension hierarchies and aggregate functions. QB4OLAP was proposed to remedy this. However, QB4OLAP requires extensive manual annotation and users must still write queries in SPARQL, the standard query language for RDF, which typical OLAP users are not familiar with. In this demo, we present QB2OLAP, a tool for enabling OLAP on existing QB data. Without requiring any RDF, QB(4OLAP), or SPARQL skills, it allows semi-automatic transformation of a QB data set into a QB4OLAP one via enrichment with QB4OLAP semantics, exploration of the enriched schema, and querying with the high-level OLAP language QL that exploits the QB4OLAP semantics and is automatically translated to SPARQL.Peer ReviewedPostprint (author's final draft

Towards an Efficient Evaluation of General Queries

Database applications often require to evaluate queries containing quantifiers or disjunctions, e.g., for handling general integrity constraints. Existing efficient methods for processing quantifiers depart from the relational model as they rely on non-algebraic procedures. Looking at quantified query evaluation from a new angle, we propose an approach to process quantifiers that makes use of relational algebra operators only. Our approach performs in two phases. The first phase normalizes the queries producing a canonical form. This form permits to improve the translation into relational algebra performed during the second phase. The improved translation relies on a new operator - the complement-join - that generalizes the set difference, on algebraic expressions of universal quantifiers that avoid the expensive division operator in many cases, and on a special processing of disjunctions by means of constrained outer-joins. Our method achieves an efficiency at least comparable with that of previous proposals, better in most cases. Furthermore, it is considerably simpler to implement as it completely relies on relational data structures and operators