Knowledge Based System using Ontology for Accessing Sentiments of Indian Railways Tweets

The interactions in social networks like Twitter are reflecting sentiments of people at large. Especially opinion mining has wherewithal to provide social feedback that complements traditional feedback for making strategic decisions. The sentiments data is basically stored in the Relational Data Base. But the demerits of this data base is that it comes back with an answer at most once and it is very difficult to address complex queries over the data, lagging inherent properties such as transitivity or symmetry. It also has closed world assumption i.e., what is not known to be true in the data base is by default considered as false because knowledge represented in the data base is assumed to be complete. To overcome all these demerits, we have developed knowledge-based system using ontology, for accessing sentiments of Indian Railways tweets. Indian Railways (IR) is very huge organization which consistently strives to improve its services from time to time. In our prior work, a framework was proposed and implemented using sentiment analysis for Indian railways tweets. The results were grouped into clusters based on five attributes pertaining to IR such as Cleanliness, Staff Behaviour, Punctuality, Security and Timeliness. These clusters are being updated from time to time to reflect up to date social feedback. However, the problem with the existing system is that, its accessibility and ease of use to stakeholders is not easy. So, we proposed a knowledge based system which will represent clusters for a universal and interoperable data representation that is from database to RDF schema to ontology and apply inference rules and query using SPARQL. This system is accessible to humans and also programs in heterogeneous Machine-to-Machine (M2M) environments. We proposed a methodology to achieve this and the knowledge is made available for further processing and stakeholders can access. The proposed system is evaluated with a prototype application and found to be useful and flexibleV

Investigating the diversity of Type Ia supernova spectra with the open-source relational data base kaepora

We present a public, open-source relational data base (we name kaepora) containing a sample of 4975 spectra of 777 Type Ia supernovae (SNe Ia). Since we draw from many sources, we significantly improve the spectra by inspecting these data for quality, removing galactic emission lines and cosmic rays, generating variance spectra, and correcting for the reddening caused by both MW and host-galaxy dust. With our data base, we organize this homogenized data set by 56 unique categories of SN-specific and spectrum-specific metadata. With kaepora, we produce composite spectra of subpopulations of SNe Ia and examine how spectral features correlate with various SN properties. These composite spectra reproduce known correlations with phase, light-curve shape, and host-galaxy morphology. With our large data set, we are also able to generate fine-grained composite spectra simultaneously over both phase and light-curve shape. The colour evolution of our composite spectra is consistent with other SN Ia template spectra, and the spectral properties of our composite spectra are in rough agreement with these template spectra with some subtle differences. We investigate the spectral differences of SNe Ia that occur in galaxies with varying morphologies. Controlling for light-curve shape, which is highly correlated with host-galaxy morphology, we find that SNe Ia residing in late-type and early-type galaxies have similar spectral properties at multiple epochs. However for SNe Ia in these different environments, their spectra appear to have Ca ii near-infrared triplet features that have slightly different strengths. Although this is apparent in the composite spectra and there is some difference in the populations as seen by individual spectra, this difference is not large enough to indicate differences in the underlying populations. All individual spectra and metadata are available in our open-source data base kaepora along with the tools developed for this investigation to facilitate future investigations of SN Ia properties.

INTEGRATION OF QUERIES TO HETEROGENEOUS DATA SOURCES USING LINQ TECHNOLOGY

Nowadays, the data are available in a variety of formats such as relational data-base tables, xml files, rdf files or simply text files. Database systems have their own query languages and tools for the manipulation of data. On the other hand, most of todays applications are created in languages based on the object-oriented paradigm. From the level of the programming language it is important to use different sources of data in a uniform manner. The paper discusses the elements of the various query languages such as SQL XQuery or SPARQL. And then shows the capabilities of LINQ and its role in the creation of abstract data access layer. Then the possibilities of LINQ extension are discussed. As the example, design and implementation of LINQ provider for Allegro is presented.

Big Data Analysis

The data management industry has matured over the last three decades, primarily based on Relational Data Base Management Systems (RDBMS) technology. The amount of data collected and analyzed in enterprises has increased several folds in volume, variety and velocity of generation and consumption, organizations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. The data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives. There are a number of challenges that must be addressed to allow us to exploit the full potential of Big Data. This article highlights the key technical challenges of Big Data.

An Instrument to Predict Fall Risk—Project StepWiz

Falls are the most common cause of injuries and the primary etiology for accidental deaths in the elderly population. The ability to quickly take a step is of paramount importance in maintaining balance. Previous research has shown a significant correlation between the time it takes to execute a step and the risk of experiencing a future fall. Consequently, a method that can quickly and accurately measure step behavior may be used to identify individuals with an increased risk of falling. The current project has built a prototype device that can be used in a clinical setting to easily and efficiently measure parameters of step execution. The step is performed under either single task (motor task only) or dual task conditions (motor task while performing an attention demanding cognitive task). Data can be stored in a relational data base and a clinical report that reflects fall risk can be printed. The current project is part of the Swedish PIEp initiative (Product Innovation Engineering Program), a federally and industry supported program that promotes innovation and technology commercialization in engineering education through development of innovation knowledge, experience and education including exchange of students and personnel between industry and academia on a national and international level.