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Open Access
Modelling and short term forecasting of flash floods in an urban environment
(Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Ogale, Suraj; Srivastava, Sanjay
Rapid urbanization, climate change and extreme rainfall has resulted in a growingnumber of cases of urban flash floods. It is important to predict the occurrenceof flood so that the aftermath of the flood can be minimized. Flood forecasting isa major exercise performed to determine the chances of a flood when suitableconditions are present. Short term forecasting or nowcasting is a dominant techniqueused in urban cities for prediction of the very near future incident up to sixhours. In orthodox methods of flood forecasting, current weather conditions areexamined using conventional methods such as use of radar, satellite imaging andcomplex calculation involving complicated mathematical equations.Recent developments in Information and Communication Technology(ICT) andMachine Learning(ML) has helped us to study this hydrological problem alongwith many real world situation in different perspective. The main aim of thisthesis is to design a theoretical model that accounts parameters causing an urbanflash flood and develop a prediction tool for the forecasting of near future event.To test the soundness of a model, data syntheses is performed and the results areseen using the artificial neural network.
Open Access
Service integration on social network
(Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Patel, Mehul; Chaudhary, Sanjay; Bise, Minal
Microblogging services are part of social network platforms, which allow people to exchange short messages. Social networks provide people to play an active role in collecting, analyzing and reporting news and information. People can use social network platform for marketing, buying and selling of their products. A sellers can tweet regarding product information including links of related photos, videos etc. A buyer can show interest in the product by means of tweets. Social network can be used as a mechanism to bring sellers and buyers closer. It provides a common platform for buyers and sellers to sell and buy their products. Microblogs can be parsed and analyzed to generate useful suggestions, e.g. sellers can be informed about potential buyers to get higher profit. Such information can be used to generate classified information to help users to take decision, e.g. minimum expected price of a crop that sellers expect in a given region. Microblogs can be written in different regional languages. Agro-produce marketing information can be processed and then stored in RDF/RDF(S) and OWL data store. SPARQL and conjunctive queries with pellet like reasoner or SPARQL-DL can be used to generate classified summarized information from RDF/RDF(S) and OWL data store.
Open Access
Learning to rank: using Bayesian networks
(Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Gupta, Parth; Mjumder, Prasenjit; Mitra, Suman K.
Ranking is one of the key components of an Information Retrieval system. Recently supervised learning is involved for learning the ranking function and is called 'Learning to Rank' collectively. In this study we present one approach to solve this problem. We intend to test this problem in di erent stochastic environment and hence we choose to use Bayesian Networks for machine learning. This work also involves experimentation results on standard learning to rank dataset `Letor4.0'[6]. We call our approach as BayesNetRank. We compare the performance of BayesNetRank with another Support Vector Machine(SVM) based approach called RankSVM [5]. Performance analysis is also involved in the study to identify for which kind of queries, proposed system gives results on either extremes. Evaluation results are shown using two rank based evaluation metrics, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG).
Open Access
SPARQL query optimization
(Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Singh, Rohit Kumar; Chaudhary, Sanjay
Query Optimization is the process of selecting the most efficient query evaluation plan among the many strategies possible for processing a given query, especially if the query is complex. The users are not expected to write their queries in such a way so that they can be processed efficiently; rather it is expected from system to construct a query evaluation plan that minimizes the cost of query evaluation. In any query optimization, the goal is to find the execution plan which is expected to return the result set without actually executing the query or subparts with optimal cost. Query engines for ontological data mostly execute user queries without considering any optimization. Especially for large ontologies,optimization techniques are required to ensure that query results are delivered within reasonable time. SPARQL can be used to express queries across diverse data sources, whether the data is stored natively as RDF or viewed as RDF via middleware. So, Query optimization may speed up SPARQL query answering by knowledge intensive reformulation. In our research work, we have proposed learning approach to solve this problem. In our approach, the learning is triggered by user queries. Then the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and properties to construct rules from a ontology with many concepts. The learned semantic rules are effective for optimization of SPARQL query because they match query patterns and reflect data regularities.

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