Abstract

Placement of items on the shelf space of retail stores significantly impacts the revenue of the retailer. Given the prevalence and popularity of medium-to-large-size retail stores, several research efforts have been made towards facilitating item/itemset placement in retail stores for improving retailer revenue. However, they do not consider the issue of urgency of sale of individual items. Hence, they cannot efficiently index, retrieve and place high-revenue itemsets in retail store slots in an urgency-aware manner. Our key contributions are two-fold. First, we introduce the notion of urgency for retail itemset placement. Second, we propose the urgency-aware URI index for efficiently retrieving high-revenue and urgent itemsets of different sizes. We discuss the URIP itemset placement scheme, which exploits URI for improving retailer revenue. We also conduct a performance evaluation with two real datasets to demonstrate that URIP is indeed effective in improving retailer revenue w.r.t. existing schemes.

Abstract

Billboard advertisement is one of the dominant modes of traditional outdoor advertisements. Given a set of billboards, there is an opportunity to improve the revenue of the billboard operator by satisfying the advertising demands of an increased number of clients by exploiting the user trajectory data. Our main contributions are three-fold. First, we introduce the problem of billboard advertisement allocation for improving the billboard operator revenue. Second, we propose an efficient user trajectory-based transactional framework using coverage pattern mining for addressing the billboard advertisement allocation problem. Third, we conduct a performance study with a real dataset to show the effectiveness of the proposed framework.

Abstract

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large transactional databases. Typically, a given transactional database D gets updated due to the addition and deletion of transactions. Consequently, some of the previously discovered patterns may become invalid, while some new patterns may emerge. This has motivated significant research efforts in the area of incremental mining. The goal of incremental mining is to efficiently mine patterns when D gets updated with additions and/or deletions of transactions as opposed to mining all of the patterns from scratch. Incidentally, active research efforts are being made to develop incremental pattern mining algorithms for extracting frequent patterns, sequential patterns and utility patterns. Another important type of pattern is the coverage pattern (CP), which has significant applications in areas such as banner advertising, search engine advertising and visibility mining. However, none of the existing works address the issue of incremental mining for extracting CPs. In this regard, the main contributions of this work are twofold. First, we introduce the problem of incremental mining of CPs. Second, we propose an approach, designated as Comprehensive Coverage Pattern Mining, for efficiently extracting CPs under the incremental paradigm. We have also performed extensive experiments using two real click-stream datasets and one synthetic dataset to demonstrate the overall effectiveness of our proposed approach.

Abstract

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.

Abstract

Placement of items on the shelf space of retail stores signifcantly impacts the revenue of the retailer. Given the prevalence and popularity of medium-to-large-size retail stores, several research efforts have been made towards facilitating item/itemset placement in retail stores for improving retailer revenue. However, they do not consider the issue of urgency of sale of individual items. Hence, they cannot efficiently index, retrieve and place high-revenue itemsets in retail store slots in an urgency-aware manner. Our key contributions are two-fold. First, we introduce the notion of urgency for retail itemset placement. Second, we propose the urgency-aware URI index for efficiently retrieving high-revenue and urgent itemsets of different sizes. We discuss the URIP itemset placement scheme, which exploits URI for improving retailer revenue. We also conduct a performance evaluation with two real datasets to demonstrate that URIP is indeed effective in improving retailer revenue w.r.t. existing schemes.

Abstract

Billboard advertisement is among the dominant modes of outdoor advertisements. The billboard operator has an opportunity to improve its revenue by satisfying the advertising demands of an increased number of clients by means of exploiting the user trajectory data. Hence, we introduce the problem of billboard advertisement allocation for improving the billboard operator revenue, and propose an efficient user trajectory-based transactional framework using coverage pattern mining. Our experiments validate the effectiveness of our framework.

Abstract

Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Weighted Frequent Neighborhood Itemsets (WFNI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an WFNI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM2.5. The generated WFNIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Pattern-growth, has also been presented to find all WFNIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.

Abstract

Fine-grained object recognition refers to a subordinate level of object recognition such as recognition of bird species and car models. It has become crucial for recognition of previously unknown classes. While fine-grained object recognition has seen unprecedented progress with the advent of neural networks, many of the existing works are cost-sensitive as they are acutely picture-dependent and fail without the adequate number of quality pictures. Efforts have been made in the literature for a picture-independent recognition with hybrid human-computer recognition methods via single question answering with a human-in-the-loop. To this end, we propose an improved batch-based local question answering method for making the recognition efficient and picture-independent. When pictures are unavailable, at each time-step, the proposed method mines a batch of binary cluster-centric local questions to pose to a human-in-the-loop and incorporates the responses received to the questions into the model. After a preset number of time-steps, the most probable class of the target object is returned as the final prediction. When pictures are available, our model facilitates the plug-in of computer vision algorithms into the framework for better performance. Experiments on three challenging datasets show significant performance improvement with respect to accuracy and computation time as compared to the existing schemes.

Abstract

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large databases. However, developing fast and efficient parallel algorithms for handling large volumes of data is a challenging task. The MapReduce framework enables the distributed processing of huge amounts of data in large-scale distributed environment with robust fault-tolerance. In this paper, we propose a parallel algorithm for extracting coverage patterns. The results of our performance evaluation with real-world and synthetic datasets demonstrate that it is indeed feasible to extract coverage patterns effectively under the MapReduce framework.

Abstract

Due to advances in technology for better output/production, there has been an increased demand for technology with optimized resource utilization in order to conserve resources. Advancements in sensors have improved resource optimization in many fields such as electric power consumption, water management and quality management. In this regard, air conditioner (AC) management is an important aspect of electric power consumption. In this paper, we address the issue of reducing power consumption for air conditioning of a given room. In previous works, the whole room is maintained at a fixed required temperature by inherently assuming that all of the users prefer the same temperature; this is generally not true in practice. In this paper, we propose a sensor-based framework to place and manage ACs for maintaining diverse temperature zones in a given layout to reduce power consumption. Through a simulation study, we demonstrate that the proposed framework indeed has the potential to reduce power consumption significantly as compared to the naive approach by achieving user satisfaction.