Abstract

Periodic-frequent patterns (or itemsets) are an important class of regularities that exist in a transactional database. Finding these patterns involves discovering all frequent patterns that satisfy the user specified maximum periodicity constraint. This constraint controls the maximum inter-arrival time of a pattern in a database. The time complexity to measure periodicity of a pattern is O(n), where n represents the number of timestamps at which the corresponding pattern has appeared in a database. As unusually represents a high value in voluminous databases, determining the periodicity of every candidate pattern in the itemset lattice makes the periodic-frequent pattern mining a computationally expensive process. This paper introduces a novel approach to address this problem. Our approach determines the periodic interestingness of a pattern by adopting greedy search. The basic idea of our approach is to discover all periodic-frequent patterns by eliminating a periodic patterns based on suboptimal solutions. The best and worst case time complexities of our approach to determine the periodic interestingness of a frequent pattern are O(1) and O(n), respectively. We introduce two pruning techniques and propose a pattern-growth algorithm to find these patterns efficiently. Experimental results show that our algorithm is runtime efficient and highly scalable as well.

Abstract

Recommender system (RS) helps customers to select appropriate products from millions of products and has become a key component in e-commerce systems. Collaborative filtering (CF) based approaches are widely employed to build RSs. In CF, recommendation to the target user is computed after forming the corresponding neighbour-hood of users. Neighborhood of a target user is extracted based on the similarity between the product rating vector of the target user and the product rating vectors of individual users. In CF, the methodology employed for neighborhood formation influences the performance. In this paper, we have made an effort to improve the performance of CF by proposing a different approach to compute recommendations by considering two kinds of neighborhood. One is the neighborhood by considering the product ratings of the user as a single vector and the other is based on the neighborhood of the corresponding virtual users. For the target user,the virtual users are formed by dividing the ratings based on the category of products. We have proposed a combined approach to compute better recommendations by considering both kinds of neighborhoods.The experiments results on real world Movie Lens dataset show that the proposed approach improves the performance over CF.

Abstract

Building efficient search systems to extract relevant in-formation from a huge volume of legal judgments is a research is-sue. In the literature, efforts are being made to build efficient search systems in the legal domain by extending information retrieval approaches. We are making efforts to investigate improved approaches to extract relevant legal judgments for a given input judgment by exploiting text and citation information of legal judgments. Typically,legal judgments are very large text documents and contain several intricate legal concepts. In this paper, we analyze how the paragraph-level and citation information of the judgments could be exploited for retrieving relevant legal judgments for the given judgment. In this paper, we have proposed improved ranking approach to find the relevant legal judgments of a given judgment based on the similarity be-tween the paragraphs of the judgments by employing Okapi retrieval model and citation information. The user evaluation study on legal judgments data set delivered by Supreme Court of India shows that the proposed approach improves the ranking performance over the baseline approach. Overall, the analysis shows that there is a scope to exploit the paragraph-level and citation information of the judgments to improve the search performance.

Abstract

Periodic-frequent pattern mining involves finding all frequent patterns that have occurred at regular intervals in a transactional database. The basic model considers a pattern as periodic-frequent, if it satisfies the user-specified minimum support (min Sup) and maximum periodicity (maxP er) constraints. The usage of a single minSupandmaxP erfor an entire database leads to the are-item problem.When confronted with this problem in real-world applications, researchers have tried to address it using the item-specific minSupandmaxP er con-straints. It was observed that this extended model still generates a sig-nificant number of uninteresting patterns, and moreover, suffers from the issue of specifying item-specific minSupandmaxP er constraints.This paper proposes a novel model to address the rare-item problem in periodic-frequent pattern mining. The proposed model considers a pat-tern as interesting if its support and periodicity are close to that of its individual items. The al l-confidence is used as an interestingness measure to filter out uninteresting patterns in support dimension. In addition, anew interestingness measure, called periodic-al l-confidence, is being pro-posed to filter out uninteresting patterns in periodicity dimension. We have proposed a model by combining both measures and proposed a pattern-growth approach to resolve the rare-item problem and extract interesting periodic-frequent patterns. Experimental results show that the proposed model is efficient

Abstract

Periodic-frequent patterns are an important class of regularities which exists in a transactional database. A frequent pattern is called periodic-frequent if it appears at regular intervals in a transactional database. In the literature, a model of periodic-frequent patterns was proposed and pattern growth like approaches to extract patterns are being explored. In these approaches, a periodic-frequent pattern tree is built in which a transaction-id list is maintained at each path’s tail-node. As the typical size of transactional database is very huge in the modern e-commerce era, extraction of periodic-frequent patterns by maintaining transaction-ids in the tree requires more memory.In this paper, to reduce the memory requirements, we introduced a notion of period summary by capturing the periodicity of the patterns in a sequence of transaction-ids. While building the tree,the period summary of the transactions is computed and stored at the tail-node of the tree instead of the transaction-ids. We have also proposed a merging framework for period summaries for mining periodic-frequent patterns. The performance could be improved significantly as the memory required to store the period summaries is significantly less than the memory required to store the transaction-id list. Experimental results show that the pro-posed approach reduces the memory consumption significantly and also improves the runtime efficiency considerably over the existing approaches.

Abstract

A method, non-transitory computer readable medium, and apparatus for receiving data from a cloud database. One or more queries requesting data from the cloud database are received. The one or more queries are converted from a row-store database query into a column-store database query. An optimal join plan is identified for the one or more queries using a cost based optimizer based on metadata for one or more relations in the cloud database. The optimal join plan is executed using a cloud application programming interface.

Abstract

In a traditional ER model, once we specify a subclass or superclass relationship, any changes to that relationship are treated as schema evolution. Further, ER models are rigid in the sense that once a relationship type is specified across a set of entity types, an instance of relationship type occur when one instance of all participating entity types are specified. Therefore, it is difficult to introduce in a simplified manner all relationship types across subsets of given set of entity types. In this paper, we provide mechanisms to model in our extended ER model: (i) specification of dynamic relationship types across subsets of instances of entity types, (ii) a simplified specification of relationships across subsets of given set of entity types, and (iii) mapping our extended ER model to relational database schema. We also show through an e-contract example the utility of our extended ER model.

Abstract

Syntactic parsing in NLP is the task of working out the grammatical structure of sentences. Some of the purely formal approaches to parsing such as phrase structure grammar, dependency grammar have been successfully employed for a variety of languages. While phrase structure based constituent analysis is possible for fixed order languages such as English, dependency analysis between the grammatical units have been suitable for many free word order languages. These approaches rely on identifying the linguistic units based on their formal syntactic properties and establishing the relationships between such units in the form of a tree. Instead, we characterize every morphosyntactic unit as a mapping between form and function on the lines of Construction Grammar and parsing as identification of dependency relations between such conceptual units. Our approach to parser annotation shows an average MALT LAS score of 82.21% on Tamil gold annotated corpus of 935 sentences in a five-fold validation experiment.

Abstract

This paper explores P¯an. inian Grammar (PG) as an information processing device in terms of ‘how’, ‘how much’ and ‘where’ languages encode information. PG is based on a morphologically rich language, Sanskrit. We apply PG on English and see how the P¯an. inian perspective would deal with it from the information theoretical point of view and its effectiveness in machine translation. We analyze English phrases defining sup (nominal inflections) and ti ˙n (finite verb inflections) and compare them with the notion of pada (an inflected word form) and samasta-pada (compound) in Sanskrit. Sanskrit encodes relations between nouns and adjectives and nouns in apposition through agreement between gender, number and case markers, whereas English encodes them through positions. As a result, constituents are formed. It appears that an English phrase contains more than one pada, hence, cannot be similar to a pada. However, we show the linguistic similarities between a pada, samasta-pada and ‘phrase’.

Abstract

In this paper, we present hybrid approaches for pronominal reference type (abstract or concrete) identification and event anaphora resolution for Hindi. Pronominal reference type identification is one of the important parts for any anaphora resolution system as it helps anaphora resolver in optimal feature selection based on pronominal reference types. We use language specific rules and features in set of classifiers (ensemble learning) for pronominal type identification. We discuss event referring anaphors (pronouns) and their resolution using Paninian dependency grammar, language syntax, proximity of events, etc. We achieved around 9̃0% accuracy in the pronominal reference type identification and around 7̃1% F-score in the event anaphora resolution on Hindi dependency tree-bank corpus.