Abstract

“Rare item problem” is a fundamental problem in pattern mining. It represents the inability of a pattern mining model to discover the knowledge about frequent and rare items in a database. In the literature, researchers advocated the usage of null-invariant measures as they disclose genuine correlations without being influenced by the object co-absence in the database. Since the existing null-invariant measures consider only an item’s frequency and disregard its temporal occurrence information, they are inadequate to address the rare item problem faced by the partial periodic pattern model. This paper proposes a novel null-invariant measure, called relative periodic-support, to find the patterns containing both frequent and rare items in non-uniform temporal databases. We also introduce an efficient pattern-growth algorithm to find all desired patterns in a database. Experimental results demonstrate that our algorithm is efficient.

Abstract

Exposure to ambient Particulate Matter (PM) of air pollution is a leading risk factor for morbidity and mortality. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which are expensive to acquire as well as to maintain. Moreover, such stations are typically sparsely deployed, thereby resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative for improving the granularity of monitoring. We are making an effort to explore the framework for air quality data collection by employing low-cost sensors. In this paper, we have reported our initial experiences and observations concerning PM data collection for four months starting from October 2021 in the city of Hyderabad in India.

Abstract

Air pollution is a major threat to human health in India. More than three-quarters of the people in India are exposed to pollution levels higher than the limits recommended by the National Ambient Air Quality Standards in India and significantly higher than those recommended by the World Health Organization. Despite the poor air quality, the monitoring of air pollution levels is limited even in large urban areas in India and virtually absent in small towns and rural areas. The lack of data results in a minimal understanding of spatial and temporal patterns of air pollutants at local and regional levels. This paper is the second in a planned series of papers presenting particulate air pollution trends monitored in small cities and towns in India. The findings presented here are important for framing state and regional level policies for addressing air pollution problems in urban areas, and achieve the sustainable development goals (SDGs) linked to public health, reduction in the adverse environmental impact of cities, and adaptation to climate change, as indicated by SDGs 3.9, 11.6 and 11.b.

Abstract

It is a well-established fact in the retail industry that the placement of products on the shelves of the retail store has a significant impact on the revenue of the retailer. Given that customers tend to purchase sets of items together (i.e., itemsets) instead of individual items, it becomes a necessity to strategically place itemsets on the shelves of the retail store for improving retailer revenue. Furthermore, in practice, customers belong to different market segments based on factors such as purchasing power, demographics and customer behaviour. Existing research efforts do not address the issue of market segmentation w.r.t. itemset placement in retail stores. Consequently, they fail to efficiently index, retrieve and place high-utility itemsets in the retail slots in a market segmentation aware manner. In this work, we introduce the problem of market segmentation aware itemset placement for retail stores. Moreover, we propose a market segmentation aware retail itemset placement framework, which takes high-utility itemsets as input. Our performance evaluation with two real datasets demonstrates that our proposed framework is indeed effective in improving retailer revenue w.r.t. existing schemes.

Abstract

Given a coarse satellite image and a fine satellite image of a particular location taken at the same time, the high-resolution spatiotemporal image fusion technique involves understanding the spatial correlation between the pixels of both images and using it to generate a finer image for a given coarse (or test) image taken at a later time. This technique is extensively used for monitoring agricultural land cover, forest cover, etc. The two key issues in this technique are: (i) handling missing pixel data and (ii) improving the prediction accuracy of the fine image generated from the given test coarse image. This paper tackles these two issues by proposing an efficient method consisting of the following three basic steps: (i) imputation of missing pixels using neighborhood information, (ii) cross-scale matching to adjust both the Point Spread Functions Effect (PSF) and geo-registration errors between the course and high-resolution images, and (iii) error-based modulation, which uses pixel-based multiplicative factors and residuals to fix the error caused due to modulation of temporal changes. The experimental results on the real-world satellite imagery datasets demonstrate that the proposed model outperforms the state-of-art by accurately producing the high-resolution satellite images closer to the ground truth.

Abstract

Billboard advertisement is one of the dominant modes of traditional outdoor advertisements. A billboard operator manages the ad slots of a set of billboards. Normally, a user traversal is exposed to multiple billboards. 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 and ensuring that a user gets exposed to different ads on the billboards during the traversal. In this paper, we propose a framework to improve the revenue of the billboard operator by employing transactional modeling in conjunction with pattern mining. 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 improving the revenue of the billboard operator. Third, we conduct a performance study with a real dataset to demonstrate the effectiveness of our proposed framework.

Abstract

No Results Found

Abstract

India Meteorological Department (IMD) has started block-level level agromet advisory (AA) service from the year 2015 and is currently operating in a few blocks of each state across India. In a block-level AA service, on every Tuesday and Friday, AA is being prepared for each block based on the block-level Medium Range weather Forecast (MRF). In this paper, we propose a framework to improve the preparation of block- level AA by modeling a weather situation as “Category-based Weather Condition (CWC)” and exploiting both “temporal reuse” and “spatial reuse” of AA based on the similarity among CWCs. The weather data analysis for 12 blocks of Telangana by considering the phenophase-specific CWCs of Rice crop showed that there is a scope to improve the efficiency of block-level AA bulletin preparation process by exploiting reuse

Abstract

Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion.

Abstract

Alongside revenue maximization, retailers seek to offer a diverse range of items to facilitate sustainable revenue generation in the long run. Moreover, customers typically buy sets of items, i.e., itemsets, as opposed to individual items. Therefore, strategic placement of diversified and high-revenue itemsets is a priority for the retailer. Research efforts made towards the extraction and placement of high-revenue itemsets in retail stores do not consider the notion of diversification. Further, candidate itemsets generated using existing utility mining schemes usually explode; which can cause memory and retrieval- time issues. This work makes three key contributions. First, we propose an efficient framework for retrieval of high-revenue itemsets with a varying size and a varying degree of diversification. A higher degree of diversification is indicative of fewer repetitive items in the top-revenue itemsets. Second, we propose the kUI (k Utility Itemset) index for quick and efficient retrieval of diverse top-λ high-revenue itemsets. We also propose the HUDIP (High-Utility and Diversified Itemset Placement) scheme, which exploits our proposed kUI index for placement of high-revenue and diversified itemsets. Third, our extensive performance study with both real and synthetic datasets demonstrates the effectiveness of our proposed HUDIP scheme in efficiently determining high-revenue and diversified itemsets.