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Retailer revenue is significantly impacted by item placement in retail stores. Notably, placement of items in the premium slots (i.e., slots with increased visibility/accessibility) improves the probability of sale w.r.t. item placement in non-premium slots. Moreover, customers often tend to buy sets of items (i.e., itemsets) instead of individual purchases. In this paper, we address the problem of maximizing retailer revenue by determining the placement of itemsets in different types of slots with varied premiumness. Our key contributions are as follows. First, we introduce the notion of premiumness of retail slots and discuss the issue of itemset placement in slots with varied premiumness. Second, we propose two efficient schemes, namely ${P}$ remiumness and ${R}$ evenue-based ${I}$ temset ${P}$ lacement (PRIP) and ${P}$ remiumness and ${A}$ verage ${R}$ evenue-based ${I}$ temset ${P}$ lacement (PARIP), for placing itemsets with varying revenue in slots with varied premiumness. Third, we perform a detailed performance analysis using both real and synthetic datasets to showcase the effectiveness of our proposed schemes. We also perform a comprehensive mathematical analysis of our proposed schemes w.r.t. the complexity analysis.

Utility mining has been emerging as an important area in data mining. While existing works on utility mining for retail businesses have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. Here, the slot size of a given item is the number of (integer) slots occupied by that item on the retail store shelves. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this paper considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as Slot Type Utility (STU) index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using both real and synthetic datasets to demonstrate the overall effectiveness of the STU index in quickly retrieving the top-utility itemsets by considering a placement scheme in terms of execution time and utility (net revenue) as compared to recent existing schemes.

In retail stores, the placement of items on the shelf space significantly impacts the sales of items. In particular, the probability of sales of a given item is typically considerably higher when it is placed in a premium (i.e., highly visible/easily accessible) slot as opposed to a non-premium slot. In this paper, we address the problem of maximizing the revenue for the retailer by determining the placement of the itemsets in different types of slots with varied premiumness such that each item is placed at least once in any of the slots. We first propose the notion of premiumness of slots in a given retail store. Then we discuss a framework for efficiently identifying itemsets from a transactional database and placing these itemsets by mapping itemsets with different revenue to slots with varied premiumness for maximizing retailer revenue. Our performance evaluation on both synthetic and real datasets demonstrate that the proposed scheme indeed improves the retailer revenue by up to 45% w.r.t. a recent existing scheme.

In addition to maximizing the revenue, retailers also aim at diversifying product offerings for facilitating sustainable revenue generation in the long run. Thus, it becomes a necessity for retailers to place appropriate itemsets in a limited k number of premium slots in retail stores for achieving the goals of revenue maximization and itemset diversification. In this regard, research efforts are being made to extract itemsets with high utility for maximizing the revenue, but they do not consider itemset diversification i.e., there could be duplicate (repetitive) items in the selected top-utility itemsets. Furthermore, given utility and support thresholds, the number of candidate itemsets of all sizes generated by existing utility mining approaches typically explodes. This leads to issues of memory and itemset retrieval times. In this paper, we present a framework and schemes for efficiently retrieving the top-utility itemsets of any given itemset size based on both revenue as well as the degree of diversification. Here, higher degree of diversification implies less duplicate items in the selected top-utility itemsets. The proposed schemes are based on efficiently determining and indexing the top-λ high-utility and diversified itemsets. Experiments with a real dataset show the overall effectiveness and scalability of the proposed schemes in terms of execution time, revenue and degree of diversification w.r.t. a recent existing scheme.

Utility mining has been emerging as an important area in data mining. While existing works on utility mining have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this paper considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three-fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as the STUI index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using real datasets to demonstrate the overall effectiveness of the proposed indexing scheme in terms of execution time and utility (net revenue) as compared to a recent existing scheme.

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 neighbourhood 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 MovieLens dataset show that the proposed approach improves the performance over CF.

Developing efficient approaches to extract relevant information from a collection of legal judgments is a research issue. Legal judgments contain citations in addition to text. It can be noted that the link information has been exploited to build efficient search systems in web domain. Similarly, the citation information in legal judgments could be utilized for efficient search. In this paper, we have proposed an approach to find similar judgments by exploiting citations in legal judgments through cluster analysis. As several judgments have few citations, a notion of paragraph link is employed to increase the number of citations in the judgment. User evaluation study on the judgment dataset of Supreme Court of India shows that the proposed clustering approach is able to find similar judgments by exploiting citations and paragraph links. Overall, the results show that citation information in judgments can be exploited to establish similarity between judgments.

Distributed locking is commonly adopted for performing concurrency control in distributed systems. It incorporates additional steps for handling deadlocks. This activity is carried out by methods based on wait-for-graphs or probes. The present study examines detection of conflicts based on enhanced local processing for distributed concurrency control. In the proposed "edge detection" approach, a graph-based resolution of access conflicts has been adopted. The technique generates a uniform wait-for precedence order at distributed sites for transactions to execute. The earlier methods based on serialization graph testing are difficult to implement in a distributed environment. The edge detection approach is a fully distributed approach. It presents a unified technique for locking and deadlock detection exercises. The technique eliminates many deadlocks without incurring message overheads.

Protein-protein interaction (PPI) networks are valuable biological source of data which contain rich information useful for protein function prediction. The PPI networks face data quality challenges like noise in the form of false positive edges and incompleteness in the form of missing biologically valued edges. These issues can be handled by enhancing data quality through graph transformations for improved protein function prediction. We proposed an improved method to extract similar proteins based on the notion of relaxed neighborhood. The proposed method can be applied to carry out graph transformation of PPI network datasets to improve the performance of protein function prediction task by adding biologically important protein interactions, removing dissimilar interactions and increasing reliability score of the interactions. By preprocessing PPI network datasets with the proposed methodology, the experiments conducted on both un-weighted and weighted PPI network datasets show that, the proposed methodology enhances the data quality and improves prediction accuracy over other approaches. The results indicate that the proposed approach could utilize underutilized knowledge, such as distant relationships embedded in the PPI graph.

The systems for weather observation and forecast are being operated to deal with adverse weather in general to mankind. Weather-based decision support systems (DSSs) are being build to improve the efficiency of the production systems in the domains of health, agriculture, livestock, transport, business, planing, governance and so on. The weather-based DSS provides appropriate suggestions based on the weather condition of the given period for the selected domain. In the literature, the notion of reuse is being employed in improving the efficiency of DSSs. In this paper, we have proposed a framework to identify similar weather conditions, which could help in improving the performance of weather-based DSSs with better reuse. In the proposed framework, the range of weather variable is divided into categories based on its influence on that domain. We form a weather condition for a period which is the combination of category values of weather variables. By comparing the daily/weekly weather conditions of a given year to weather conditions of subsequent years, the proposed framework identifies the extent of reuse. We have conducted the experiment by applying the proposed framework on 30 years of weather data of Rajendranagar, Hyderabad and using the categories employed by India Meteorological Department in Meteorology domain. The results show that there is a significant degree of similarity among daily and weekly weather conditions over the years. The results provide an opportunity to improve the efficiency of weather-based DSSs by improving the degree of reuse of the developed suggestions/knowledge for the corresponding weather conditions.

Schema summarization approaches are used for carrying out schema matching and developing user interfaces. Generating schema summary for any given database is a challenge which involves identifying semantically correlated elements in a database schema. Research efforts are being made to propose schema summarization approaches by exploiting database schema and data stored in the database. In this paper, we have made an effort to propose an efficient schema summarization approach by exploiting database schema and the database documentation. We propose a notion of table similarity by exploiting referential relationship between tables and the similarity of passages describing the corresponding tables in the database documentation. Using the notion of table similarity, we propose a clustering based approach for schema summary generation. Experimental results on a benchmark database show the effectiveness of the proposed approach.

A model of crop specific virtual labs is proposed to improve practical agricultural education by considering the agricultural education system in India. In agricultural education, the theoretical concepts are being imparted through class room lectures and laboratory skills are imparted in the dedicated laboratories. Further, practical agricultural education is being imparted by exposing the students to the field problems through Rural Agricultural Work Experience Program (RAWEP), experiential learning and internships. In spite of these efforts, there is a feeling that the level of practical skills exposed to the students is not up to the desired level. So we have to devise the new ways and means to enhance the practical knowledge and skills of agricultural students to understand the real-time crop problems and provide the corrective steps at the field level. Recent developments in ICTs, thus, provide an opportunity to improve practical education by developing virtual crop labs. The virtual crop labs contain a well organized, indexed and summarized digital data (text, photograph, and video). The digital data corresponds to farm situations reflecting life cycles of several farms of different crops cultivated under diverse farming conditions. The practical knowledge of the students could be improved, if we systematically expose them to virtual crop labs along with course teaching. We can employ cloud computing platform to store huge amounts of data and render to students and other stakeholders in an online manner.

Rare association rule is an association rule consisting of rare items. It is difficult to mine rare association rules with a single minimum support (minsup) constraint because low minsup can result in generating too many rules (or frequent patterns) in which some of them are uninteresting. In the literature, "maximum constraint model," which uses multiple minsup constraints has been proposed and extended to Apriori approach for mining frequent patterns. Even though this model is efficient, the Apriori-like approach raises performance problems. With this motivation, we propose an FP-growth-like approach for this model. This FP-growth-like approach utilizes the prior knowledge provided by the user at the time of input and discovers frequent patterns with a single scan on the transactional dataset. Experimental results on both synthetic and real-world datasets show that the proposed approach is efficient.

Information Requirement Elicitation (IRE) recommends a framework for developing interactive interfaces, which allow users to access database systems without having prior knowledge of a query language. An approach called ‘Query-by-Object’ (QBO) has been proposed in the literature for IRE by exploiting simple calculator like operations. However, the QBO approach was proposed by assuming that the underlying database is simple and contains few tables of small size. In this paper, we propose an enhanced QBO approach called Query-by-Topics (QBT), for designing calculator like user interfaces for large databases. We use methodologies for clustering database entities and discovering topical structures to represent objects at a higher level of abstraction. The QBO approach is then enhanced to allow users to query by topics (QBT). We developed a prototype system based on QBT and conducted experimental studies to show effectiveness of the proposed approach.