Seo-Young Noh

An XML-based methodology for parametric temporal database model implementation

Parametric data model is one of dimensional data models. It defines attributes as functions, modeling a real world object into a single tuple in a database. Such one-to-one correspondence between an object in the real world and a tuple provides various advantages in modeling dimensional data, avoiding self-joins which frequently appear in temporal data models which fragment an object into multiple tuples. Despite its modeling advantages, it is impractical to implement the parametric data model on top of conventional database systems because of the data models variable attribute sizes. However, such implementation challenge can be resolved by XML because XML is flexible for data boundaries. In this paper, we present an XML-based implementation methodology for the parametric temporal data model. In our implementation, we develop our own XML storage called CanStoreX (Canonical Storage for XML) and build the temporal database system on top of the CanStoreX.

A comparison of two approaches to utilizing XML in parametric databases for temporal data

Abstract The parametric data model captures an object in terms of a single tuple. This feature eliminates unnecessary self-join operations to combine tuples scattered in a temporal relation. Despite this advantage, this model is relatively difficult to implement on top of relational databases because the sizes of attributes are unfixed. Since data boundaries are not problematic in XML, XML can be an elegant solution to implement parametric databases for temporal data. There are two approaches to implementing parametric databases using XML: (1) a native XML database with XQuery engine, and (2) an XML storage with a temporal query language. To determine which approach is appropriate in parametric databases, we consider four questions: the effectiveness of XML in modeling temporal data, the applicability of XML query languages, the user-friendliness of the query languages, and system performances of two approaches. By evaluating the four questions, we show that the latter approach is more appropriate to utilizing XML in parametric databases.

An XML-based framework for temporal database implementation

This paper presents an XML-based approach to implementing the parametric model of temporal databases. In the parametric model, attribute values are functions of time and the entire history of an object is modeled in terms of a single tuple. This property makes it difficult to adapt the parametric model within the conventional databases. However, XML is an attractive option for implementation because data boundaries are not problematic in XML. Native XML storage with pagination is used for storing temporal data and the DOM parser for the paginated XML storage is used for data access. The primitives for the query language are implemented using the DOM parser. Many artifacts, such as parse tree and expression tree are also represented in XML. We hope that this paradigm offers an elegant approach for implementation of complex data models.

Benchmarking temporal database models with interval-based and temporal element-based timestamping

Starting from mid 1980s, there has been a debate about what data model is most appropriate for temporal databases. A fundamental choice one has to make is whether to use intervals of time or temporal elements to timestamp objects and events with the periods of validity. The advantage of using interval timestamps is that Start and End columns can be added to relations for treating them within the framework of classical databases, leading to quick implementation. Temporal elements are finite unions of intervals. The advantage of temporal elements is that timestamps become implicitly associated with values, tuples, and relations. Furthermore, since temporal elements, by design, are closed under set theoretical operations such as union, intersection and complementation, they lead to query languages that are natural. Here, we investigate the ease of use as well as system performance for the two approaches to help settle the debate.

A Model Comparison for Spatiotemporal Data in Ubiquitous Environments: A Case Study

In ubiquitous environments, many applications need to process data with time and space dimensions. Because of this, there is growing attention not only on gathering spatiotemporal data in ubiquitous environments, but also on processing such data in databases. In order to obtain the full benefits from spatiotemporal data, we need a data model that naturally expresses the properties of spatiotemporal data. In this paper, we introduce three spatiotemporal data models extended from temporal data models. The main goal of this paper is to determine which data model is less complex in the spatiotemporal context. To this end, we compare their query languages in the complexity aspect because the complexity of a query language is tightly coupled with its underlying data model. Throughout our investigations, we show that it is important to intertwine space and time dimensions and keep one-to-one correspondence between an object in the real world and a tuple in a database in order to naturally express queries in ubiquitous applications.

User-Friendly Extendibility of Two Temporal Data Models to Spatiotemporal Data Models

Data arising in real world applications have space and time dimensions that require database support. Because of this there is growing attention on spatiotemporal databases. In this paper, we introduce two temporal data models extendable to spatiotemporal data ones - point-based and temporal element-based data models. Our goal is to understand which data model is less complex than the other when space dimension is incorporated into the data models. To this end, we compare two query languages for the data models in spatiotemporal context - SQLST and ParaSQL. Since query languages are tightly coupled to their underlying data models, their complexity is influenced by their data models. We use Gutings use case in our comparison and show that ParaSQL is less complex than SQLST; suffice to say that the temporal, element-based data model is more user-friendly extendible to spatiotemporal data models if data has similar properties to Gutings use case.

Hybrid storage design for NC-94 database within the parametric data model framework

The NC-94 dataset contains the most complete records of temporal and spatial variables for climate, crop, and soil in the north central region in the United States. Scientists store and process the dataset within scientific data formats which are efficient for scientific simulations. However, it is difficult for the public to access the dataset by using ad-hoc queries because the scientific data formats are not database management systems. This paper presents a hybrid storage model to build the NC-94 database which supports ad-hoc queries. The storage is designed within the parametric data model which has advantages over conventional relational databases. In our design paradigm, the database query language supports multi-granules as well as pattern windows to extract information, reducing the needs of high-level programming languages. We expect that our proposed paradigm significantly enhance the usefulness of the existing NC-94 dataset for the public domain.

NOVEL: NO-VowEL Technique to Search Fuzzy Keyword

Information retrieval techniques play vital role in the era of information technology. Inverted index is one of the technique to retrieve the information/data related with certain keyword. This technique gives faster results to retrieve relevant document from billions of documents, which contains specified keyword. In order to support wrongly spelled keyword, many techniques have been proposed including edit distance, wild-card and n-gram. The n-gram index has language-neutral and errortolerant advantage. However, it has a drawback of large size and less performance. In this paper, we have proposed NOVEL technique to search fuzzy keyword. We have implemented and tested the proposed technique on two datasets. The result shows that NOVEL technique supports not only wrongly spelled keywords, but also reduced gram size by 40-50% than K/n-gram technique. Therefore, the proposed technique is the most efficient technique to support fuzzy keyword search.