Handling of current time in native XML databases

The introduction of Native XML databases opens many research questions related to the data models used to represent and manipulate data, including temporal data in XML. Increasing use of XML for Valid Web pages warrants an adequate treatment of now in Native XML databases. In this study, we examined how to represent and manipulate now-relative temporal data. We identify different approaches being used to represent current time in XML temporal databases, and introduce the notion of storing variables such as `now' or `UC' as strings in XML native databases. All approaches are empirically evaluated on a query that time-slices the timeline at the current time. The experimental results indicate that the proposed extension offers several advantages over other approaches: better semantics, less storage space and better response time

On the Semantics of "Now" in Databases

While "now" is expressed in SQL as CURRENT-TIMESTAMP within queries, this value cannot be stored in the database. However, this notion of an ever-increasing current-time value has been reflected in some temporal data models by inclusion of database-resident variables, such as "now," "until-changed," "â," "@" and "-." Time variables are very desirable, but their use also leads to a new type of database, consisting of tuples with variables, termed a variable database. This paper proposes a framework for defining the semantics of the variable databases of temporal relational data models. A framework is presented because several reasonable meanings may be given to databases that use some of the specific temporal variables that have appeared in the literature. Using the framework, the paper defines a useful semantics for such databases. Because situations occur where the existing time variables are inadequate, two new types of modeling entities that address these shortcomings, timestamps which we call now-relative and now-relative indeterminate, are introduced and defined within the framework. Moreover, the paper provides a foundation, using algebraic bind operators, for the querying of variable databases via existing query languages. This transition to variable databases presented here requires minimal change to the query processor. Finally, to underline the practical feasibility of variable databases, we show that database variables can be precisely specified and efficiently implemented in conventional query languages, such as SQL, and in temporal query languages, such as TSQL2.Information Systems Working Papers Serie

Concurrency Control for Perceivedly Instantaneous Transactions in Valid-Time Databases

Although temporal databases have received considerable attention as a topic for research, little work in the area has paid attention to the concurrency control mechanisms that might be employed in temporal databases. This paper describes how the notion of the current time --- also called `now' --- in valid-time databases can cause standard serialisation theory to give what are at least unintuitive results, if not actually incorrect results. The paper then describes two modifications to standard serialisation theory which correct the behaviour to give what we term perceivably instantaneous transactions; transactions where serialising T 1 and T 2 as [T 1 ; T 2 ] always implies that the current time seen by T 1 is less than or equal to the current time seen by T 2 . 1 Introduction Query languages for valid-time temporal database normally contain a notion of "currenttime " [TCG + 93, Sno95], usually represented as the value of a special variable now. While it is agreed that the value of..

ON THE SEMANTICS OF TRANSACTION TIME AND VALID TIME IN BITEMPORAL DATABASES

Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared in the past several years. While most of these have been historical databases, incorporating in some fashion a valid time dimension to the data model and the query languages, others have been rollback databases, incorporating a transaction time dimension, or bitemporal databases, incorporating both of these temporal dimensions. In this paper we address an issue that has been lacking in many of these papers, namely, a formal specification of the precise semantics of these temporal dimensions of data. We introduce the notion of reference time - the time that any operation is applied to the database state - and provide a logical analysis of the interrelationships among these three temporal dimensions. We also provide an analysis of the meaning of various variables such as now and â which have been used in many of these models without a complete specification of their semantics.Information Systems Working Papers Serie

6NF Conceptual Models and Data Warehousing 2.0

Sixth Normal Form (6NF) is a term used in relational database theory by Christopher Date to describe databases which decompose relational variables to irreducible elements. While this form may be unimportant for non-temporal data, it is certainly important when maintaining data containing temporal variables of a point-in-time or interval nature. With the advent of Data Warehousing 2.0 (DW 2.0), there is now an increased emphasis on using fully-temporalized databases in the context of data warehousing, in particular with next generation approaches such as Anchor Modeling. In this paper, we will explore the concepts of temporal data, 6NF conceptual database models, and their relationship with DW 2.0. Further, we will also evaluate Anchor Modeling as a conceptual design method in which to capture temporal data. Using these concepts, we will indicate a path forward for evaluating a possible translation of 6NF-compliant data into an eXtensible Markup Language (XML) Schema for the purpose of describing and presenting such data to disparate systems in a structured format suitable for data exchange

Translating Data Between Xml Schema and 6Nf Conceptual Models

Sixth Normal Form (6NF) is a term used in relational database theory by Christopher Date to describe databases which decompose relational variables to irreducible elements. While this form may be unimportant for non-temporal data, it is certainly important for data containing temporal variables of a point-in-time or interval nature. With the advent of Data Warehousing 2.0 (DW 2.0), there is now an increased emphasis on using fully-temporalized databases in the context of data warehousing, in particular with approaches such as the Anchor Model and Data Vault. In this work, we will explore the concepts of temporal data, 6NF conceptual database models, and their relationship with DW 2.0. Further, we will evaluate the Anchor Model and Data Vault as design methods in which to capture temporal data. Using these methods, we will define a process for translating 6NF-compliant data into a standard eXtensible Markup Language (XML) Schema which can be used to describe and present such data to disparate systems in a structured format suitable for data exchange. Further, we will discuss benefits, advantages, and limitations to using XML representations of 6NF models for the transfer of data into a data warehouse

An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights

With an unprecedented growth in the biomedical literature, keeping up to date with the new developments presents an immense challenge. Publications are often studied in isolation of the established literature, with interpretation being subjective and often introducing human bias. With ontology-driven annotation of biomedical data gaining popularity in recent years and online databases offering metatags with rich textual information, it is now possible to automatically text-mine ontological terms and complement the laborious task of manual management, interpretation, and analysis of the accumulated literature with downstream statistical analysis. In this paper, we have formulated an automated workflow through which we have identified ontological information, including nutrition-related terms in PubMed abstracts (from 1991 to 2016) for two main types of Inflammatory Bowel Diseases: Crohn’s Disease and Ulcerative Colitis; and two other gastrointestinal (GI) diseases, namely, Coeliac Disease and Irritable Bowel Syndrome. Our analysis reveals unique clustering patterns as well as spatial and temporal trends inherent to the considered GI diseases in terms of literature that has been accumulated so far. Although automated interpretation cannot replace human judgement, the developed workflow shows promising results and can be a useful tool in systematic literature reviews. The workflow is available at https://github.com/KociOrges/pytag

Bipolar querying of valid-time intervals subject to uncertainty

Databases model parts of reality by containing data representing properties of real-world objects or concepts. Often, some of these properties are time-related. Thus, databases often contain data representing time-related information. However, as they may be produced by humans, such data or information may contain imperfections like uncertainties. An important purpose of databases is to allow their data to be queried, to allow access to the information these data represent. Users may do this using queries, in which they describe their preferences concerning the data they are (not) interested in. Because users may have both positive and negative such preferences, they may want to query databases in a bipolar way. Such preferences may also have a temporal nature, but, traditionally, temporal query conditions are handled specifically. In this paper, a novel technique is presented to query a valid-time relation containing uncertain valid-time data in a bipolar way, which allows the query to have a single bipolar temporal query condition