Logical modeling of temporal data

Logical Data Modeling : Part 2

This is the second article in a series on logic-based approaches to data modeling. The first article [3] provided a brief overview of deductive databases, and illustrated how simple data models may be declared and queried in LogiQL [6], a leading edge example of deductive database system based on extended datalog [1]. The current article discusses how to declare inverse predicates, simple manda...

modeling loss data by phase-type distribution

بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...

A Logical Temporal Relational Data Model

Time is one of the most difficult aspects to handle in real world applications such as database systems. Relational database management systems proposed by Codd offer very little built-in query language support for temporal data management. The model itself incorporates neither the concept of time nor any theory of temporal semantics. Many temporal extensions of the relational model have been p...

Logical Data Expiration for Fixpoint Extensions of Temporal Logics

Logical Data Modeling of SpatioTemporal Applications: Definitions and a Model

Dealing with spatiotemporal applications at the logical phase of database design reveals a set of data peculiarities which the already existing models can not serve satisfactorily. In this paper, the ontologies of a spatiotemporal environment −namely the basic concepts of objects, attributes and relationships with spatial and temporal extent, as well as operations on them− are defined. Based on...