From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data

We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-based interfaces to facilitate easy querying of the data. These interfaces could be seen as implementing a set of "pre-canned" queries commonly used by the life science researchers that we study. The second approach is based on semantic Web technologies and is knowledge (model) driven. It utilizes a large OWL ontology and same datasets as before but associated as RDF instances of the ontology concepts. An intuitive interface is provided that allows the formulation of RDF triples-based queries. Both these approaches are being used in parallel by a team of cell biologists in their daily research activities, with the objective of gradually replacing the conventional approach with the knowledge-driven one. This provides us with a valuable opportunity to compare and qualitatively evaluate the two approaches. We describe several benefits of the knowledge-driven approach in comparison to the traditional way of accessing data, and highlight a few limitations as well. We believe that our analysis not only explicitly highlights the specific benefits and limitations of semantic Web technologies in our context but also contributes toward effective ways of translating a question in a researcher's mind into precise computational queries with the intent of obtaining effective answers from the data. While researchers often assume the benefits of semantic Web technologies, we explicitly illustrate these in practice

RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS

In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions

A Survey on Mapping Semi-Structured Data and Graph Data to Relational Data

The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today's market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases' capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from eachmodel's mapping strategies, aswell as a newresearch topic - mapping multi-model data into relational tables.Peer reviewe

A Framework for BIM Model-Based Construction Cost Estimation

This thesis presents a framework to conduct a quantity take-off (QTO) and cost estimate within the Building Information Modeling (BIM) Environment. The product of this framework is a model-based cost estimating tool. The framework addresses the cost uncertainty associated with the detailed information defining BIM model element properties. This cost uncertainty is due to the lack of available tools that address detailed QTO and cost estimation using solely a BIM platform. In addition, cost estimators have little experience in leveraging and managing information within semantic-rich BIM models. Unmanaged BIM element parameters are considered a source of uncertainty in a model-based cost estimate, therefore they should be managed and quantified as work items. A model-based system, which assists the estimators to conduct a QTO and cost estimate within the BIM environment, is developed. This system harnesses BIM element parameters to drive work items associated with the parameter’s host element. The system also captures the cost of scope not modeled in the design team’s BIM models. The system consists of four modules 1) establishing estimate requirements, 2) planning and structuring the estimate, 3) quantification and costing, and 4) model-based historical cost data collection. The complete system can produce a project cost estimate based on the 3D BIM Model. This framework is supported by a computation engine built within an existing virtual design and construction (VDC) model review software. The computation engine supports BIM authoring and reviewing BIM data. The Framework’s quantification and costing module was compared to existing methods in a case study. The outcomes of the model-based system demonstrated improved cost estimate accuracy in comparison to the BIM QTO method and improved speed compared to the traditional methods. The framework provides a systematic workflow for conducting a detailed cost estimate leveraging the parameters stored in the BIM models

Toward a Unified Timestamp with explicit precision

Demographic and health surveillance (DS) systems monitor and document individual- and group-level processes in well-defined populations over long periods of time. The resulting data are complex and inherently temporal. Established methods of storing and manipulating temporal data are unable to adequately address the challenges posed by these data. Building on existing standards, a temporal framework and notation are presented that are able to faithfully record all of the time-related information (or partial lack thereof) produced by surveillance systems. The Unified Timestamp isolates all of the inherent complexity of temporal data into a single data type and provides the foundation on which a Unified Timestamp class can be built. The Unified Timestamp accommodates both point- and interval-based time measures with arbitrary precision, including temporal sets. Arbitrary granularities and calendars are supported, and the Unified Timestamp is hierarchically organized, allowing it to represent an unlimited array of temporal entities.demographic surveillance, standardization, temporal databases, temporal integrity, timestamp, valid time