The expressive power of temporal relational query languages

We consider the representation of temporal data based on tuple and attribute timestamping. We identify the requirements in modeling temporal data and elaborate on their implications in the expressive power of temporal query languages. We introduce a temporal relational data model where N1NF relations and attribute timestamping are used and one level of nesting is allowed. For this model, a nested relational tuple calculus (NTC) is defined. We follow a comparative approach in evaluating the expressive power of temporal query languages, using NTC as a metric and comparing it with the existing temporal query languages. We prove that NTC subsumes the expressive power of these query languages. We also demonstrate how various temporal relational models can be obtained from our temporal relations by NTC and give equivalent NTC expressions for their languages. Furthermore, we show the equivalence of intervals and temporal elements (sets) as timestamps in our model. © 1997 IEEE

Towards Querying and Visualization of Large Spatio-Temporal Databases

In any database model, data analysis can be eased by extracting a smaller set of the data of interest, called subset, from the mammoth original dataset. Thus, a subset helps enhance the performance of a system by avoiding the iteration through the huge parental data in further analysis. A subset, its specification, or the formal process for its extraction can be complex. In the database community, subsets are extracted through SQL-like queries and through visualization in the Geographic Information System (GIS) community. Both are iterative processes. An SQL query can be a composition of subqueries. Each subquery can be seen as an iterative step toward the extraction of the desired subset. For this to work, subqueries should result into relations that have the same structure as the relations in a given data model. Although it may not be immediately obvious, the visualization can be iterative too. Each community works in its own compartment. Either one uses subprocesses that are only subqueries or only visual interactions. Mixing these two subprocesses would yield a more powerful expressibility in the hands of users. Parametric Data Model is well-known for handling multidimensional parametric data, such as spatial, temporal, or spatio-temporal. In the parametric approach, the object is modeled as a single tuple, creating one-to-one correspondence between an object in the real world and a tuple in the database. The parametric approach relies on its own SQL-like, but richer, query language called ParaSQL which mimics the classical SQL. However, it is simpler and avoids self-join operations; hence, enhances performance. In the parametric approach, the attribute values are defined as a function, allowing large values, also. The execution of a query in the existing prototype of the Parametric Data Model results in data out, as stream in a raw text format that cannot be queried further. This is unlike classical databases, where a subset provides additional strength to a system and the prototype lacks this potential functionality. The real power of ParaSQL lies in the where clause, and previous versions of the prototype had a very simple implementation. It is expanded further in this research work to harness its hidden potential. To perform the preliminary investigation, exploratory visual analysis is an important aspect in any spatio-temporal database system. Previous versions of the prototype of Parametric Data Model completely lacked the visualization functionality. This work ensures the output of a ParaSQL (possibly a subset) will be a relation having the same format as relations in the model rather than plain text. It also attempts to expand the power of the where clause, ensuring a clean logic and more generic nature. Some important basic steps are taken to bring a visual in a way that is conducive to the structures in Parametric Data Model. The richness of GIS visualization serves as the foundation for the visual functionality of the Parametric Data Model. The query is executed on the parametric side, while the results are visualized on GIS side. This integration equips the Parametric Data Model with visualization functionality. GIS visualization also offers a click-based selection of a subset and its persistence, which later can be consumed by Parametric Data Model also. This research work establishes a two-way communication between the two communities-Parametric Data Model and GIS- where the output of one can serve as the input for the other and is an attempt to bring them together

An XML-based implementation of the parametric model for ad-hoc query of temporal and spatiotemporal data

The parametric model is one of the data models for dimensional data. Values in the parametric model are defined as functions. Such modeling concept helps one achieve a one-to-one correspondence between objects in the real world and records in a database. One of the important requirements is that domains of values should be closed under the set theoretic operations such as union, intersection, and complementation. Because of this, ParaSQL, a query language of the parametric model, is able to mimic natural languages more closely. In this dissertation we validate and implement the parametric model for temporal and spatiotemporal data. We also develop a preliminary prototype for the users of NC-94, an interesting dataset in agriculture;Viewing values as functions leads variable-length tuples. Potentially, such values vary in size ranging from a few bytes to gigabytes and beyond. This makes implementation of the parametric model a challenging problem. To meet the challenge, we develop an XML-based storage and deploy it in our implementation. Incidentally, XML is also used for interfacing various modules and artifacts like parse tree, expression tree, and iterators to fetch data from a disk;The NC-94 dataset, mentioned above, contains the most complete record of spatiotemporal variables that characterize the dynamics of agriculture covering the north central region in the United States. To support ad-hoc query of data in its geospatial context, a novel hybrid structure is designed and implemented. We use GML to describe geospatial information. Use of GML is a good match, because it is XML-based. More importantly, it meets the set theoretic closure requirements proposed by the parametric model;Validation and implementation methodologies introduced in this dissertation will contribute to database and GIS communities. The validation demonstrates the ease of use and efficiency of the parametric model for temporal and spatiotemporal data. This should help settle a debate in temporal database community which has continued since the mid 1980s. The findings also extend to spatial and spatiotemporal data. It is an important baby-step toward full-fledged implementation of the parametric model. We hope that this work will also help bring database and GIS communities together

Discovering temporal patterns for interval-based events.

Kam, Po-shan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 89-97).Abstracts in English and Chinese.Abstract --- p.iAcknowledgements --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Mining --- p.1Chapter 1.2 --- Temporal Data Management --- p.2Chapter 1.3 --- Temporal reasoning and temporal semantics --- p.3Chapter 1.4 --- Temporal Data Mining --- p.5Chapter 1.5 --- Motivation --- p.6Chapter 1.6 --- Approach --- p.7Chapter 1.6.1 --- Focus and Objectives --- p.8Chapter 1.6.2 --- Experimental Setup --- p.8Chapter 1.7 --- Outline and contributions --- p.9Chapter 2 --- Relevant Work --- p.10Chapter 2.1 --- Data Mining --- p.10Chapter 2.1.1 --- Association Rules --- p.13Chapter 2.1.2 --- Classification --- p.15Chapter 2.1.3 --- Clustering --- p.16Chapter 2.2 --- Sequential Pattern --- p.17Chapter 2.2.1 --- Frequent Patterns --- p.18Chapter 2.2.2 --- Interesting Patterns --- p.20Chapter 2.2.3 --- Granularity --- p.21Chapter 2.3 --- Temporal Database --- p.21Chapter 2.4 --- Temporal Reasoning --- p.23Chapter 2.4.1 --- Natural Language Expression --- p.24Chapter 2.4.2 --- Temporal Logic Approach --- p.25Chapter 2.5 --- Temporal Data Mining --- p.25Chapter 2.5.1 --- Framework --- p.25Chapter 2.5.2 --- Temporal Association Rules --- p.26Chapter 2.5.3 --- Attribute-Oriented Induction --- p.27Chapter 2.5.4 --- Time Series Analysis --- p.27Chapter 3 --- Discovering Temporal Patterns for interval-based events --- p.29Chapter 3.1 --- Temporal Database --- p.29Chapter 3.2 --- Allen's Taxonomy of Temporal Relationships --- p.31Chapter 3.3 --- "Mining Temporal Pattern, AppSeq and LinkSeq" --- p.33Chapter 3.3.1 --- A1 and A2 temporal pattern --- p.33Chapter 3.3.2 --- "Second Temporal Pattern, LinkSeq" --- p.34Chapter 3.4 --- Overview of the Framework --- p.35Chapter 3.4.1 --- "Mining Temporal Pattern I, AppSeq" --- p.36Chapter 3.4.2 --- "Mining Temporal Pattern II, LinkSeq" --- p.36Chapter 3.5 --- Summary --- p.37Chapter 4 --- "Mining Temporal Pattern I, AppSeq" --- p.38Chapter 4.1 --- Problem Statement --- p.38Chapter 4.2 --- Mining A1 Temporal Patterns --- p.40Chapter 4.2.1 --- Candidate Generation --- p.43Chapter 4.2.2 --- Large k-Items Generation --- p.46Chapter 4.3 --- Mining A2 Temporal Patterns --- p.48Chapter 4.3.1 --- Candidate Generation: --- p.49Chapter 4.3.2 --- Generating Large 2k-Items: --- p.51Chapter 4.4 --- Modified AppOne and AppTwo --- p.51Chapter 4.5 --- Performance Study --- p.53Chapter 4.5.1 --- Experimental Setup --- p.53Chapter 4.5.2 --- Experimental Results --- p.54Chapter 4.5.3 --- Medical Data --- p.58Chapter 4.6 --- Summary --- p.60Chapter 5 --- "Mining Temporal Pattern II, LinkSeq" --- p.62Chapter 5.1 --- Problem Statement --- p.62Chapter 5.2 --- "First Method for Mining LinkSeq, LinkApp" --- p.63Chapter 5.3 --- "Second Method for Mining LinkSeq, LinkTwo" --- p.64Chapter 5.4 --- "Alternative Method for Mining LinkSeq, LinkTree" --- p.65Chapter 5.4.1 --- Sequence Tree: Design --- p.65Chapter 5.4.2 --- Construction of seq-tree --- p.69Chapter 5.4.3 --- Mining LinkSeq using seq-tree --- p.76Chapter 5.5 --- Performance Study --- p.82Chapter 5.6 --- Discussions --- p.85Chapter 5.7 --- Summary --- p.85Chapter 6 --- Conclusion and Future Work --- p.87Chapter 6.1 --- Conclusion --- p.87Chapter 6.2 --- Future Work --- p.88Bibliography --- p.9