Abstract

Finding (partial) periodic patterns in time series data is a challenging problem of great importance in many applications. Due to computational reasons, most previous studies in this area have focused on the efficient discovery of periodic patterns in regular time series data. Unfortunately, these studies have limited applicability because real-world data naturally exists as an irregular time series. This paper proposes a more flexible model of periodic pattern that may be present in irregular time series. Two measures, period and period-support, were employed to determine the interestingness of a pattern in a series. The former measure captures the inter-arrival times of a pattern in a series, while the latter captures the number of periodic occurrences of a pattern in a series. A novel tree structure, called Periodic Pattern tree (PP-tree), has been introduced to record the irregular occurrences of items within the series. A pattern-growth algorithm has also been presented to find all periodic patterns from PP-tree. Experimental results demonstrate that the proposed model can find useful information, and the algorithm is efficient.

Abstract

Traditional row store has the disadvantage of reading irrelevant attributes into main memory, when only a few attributes are queried and it incurs a large number of cache misses. Column store, while being cache efficient, suffers from large “stitching” costs if the number of attributes queried are large. In this paper, we introduce FAST, an intuitive, hybrid combination of both these storage mechanisms that leverages the main memory to reduce the I/O time and cache misses to efficiently serve ad-hoc read-only queries in real time. Our system acts as a commercial off-the-shelf (COTS) solution on top of existing databases. We show that on average, FAST executes queries up-to an order of magnitude faster than row storage and as much as twice as fast than column storage for ad-hoc data warehouse workloads. Our results show superior performance of FAST on multiple TPC-H queries.We also present techniques to automatically adapt the main memory layout to a changing workload.

Abstract

Finding partial periodic patterns in very large databases is a challenging problem of great importance in many real-world applications. Most previous work focused on finding these patterns in temporal (or transactional) databases and did not recognize the spatial characteristics of items. In this paper, we propose a more flexible model of partial periodic spatial pattern that may be present in spatiotemporal database. Three constraints, maximum inter-arrival time(maxIAT), minimum period-support(minPS) and maximum distance(maxDist), have been employed to determine the interestingness of a pattern in a spatiotemporal database. The maxIAT controls the maximum duration in which a pattern must reappear to consider its occurrence as periodic within the data. The minPS controls the minimum number of periodic occurrences of a pattern within the data. The maxDist controls the maximum distance between the items in a pattern. All patterns satisfying these three constraints are returned. An efficient algorithm, called SpatioTemporal-Equivalence CLAss Transformation (ST-ECLAT), has also been described to discover all partial periodic spatial patterns in a spatiotemporal database. This algorithm employs a novel smart depth-first search technique to discover desired patterns effectively. Experimental results demonstrate that the proposed algorithm is efficient. We also present a case study in which we apply our model to find useful information in the air pollution database.

Abstract

Spatial High Utility Itemset Mining (SHUIM) aims to discover all itemsets in a spatiotemporal database that satisfy the userspecified minimum utility (minU til) and maximum distance (maxDist) constraints. The popular adoption and successful industrial application of SHUIM suffers from the following two limitations: (i) Since SHUIM determines the interestingness of an itemset without taking into account its support with in the data, it is sensitive to the random noise and length of the transactions within the data. Consequently, SHUIM facilitates sporadic itemsets with high utility to be generated as SHUIs. For instance, items in long transactions can combine with each other and be generated as SHUIs. (ii) SHUIM is a computationally expensive process because the generated itemsets do not satisfy the downward closure property. This paper introduces Spatial High Utility Frequent Itemset Mining (SHUFIM) to address these two issues. A SHUI in a spatiotemporal database is said to be a SHUFI if and only if its support is no less than the user-specified minimum support (minSup) constraint. The usage of minSup not only facilitates the proposed model to be tolerant to the random noise within the data, but also facilitate us to employ additional pruning techniques to reduce the computational cost. A single scan fast algorithm has also been proposed to discover all SHUFIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also demonstrate the usefulness of the proposed model with two real-world applications.

Abstract

In this work, we consider a multi-wheeled payload transport system. Each of the wheels can be selectively actuated.When they are not actuated, wheels are free moving and do not consume battery power. The payload transport system is modeled as an actuated multi-agent system, with each wheelmotor pair as an agent. Kinematic and dynamic models are developed to ensure that the payload transport system moves as desired. We design optimization formulations to decide on the number of wheels to be active and which of the wheels to be active so that the battery is conserved and the wear on the motors is reduced. Our multi-level control framework over the agents ensures that near-optimal number of agents is active for the payload transport system to function. Through simulation studies we show that our solution ensures energy efficient operation and increases the distance traveled by the payload transport system, for the same battery power. We have built the payload transport system and provide results for preliminary experimental validation.

Abstract

Financial transactions are intimately bound with social interactions and woven into everyday economic lives. In this paper we focus on PayTM, a digital wallet, and a specific set of users of PayTM, street vendors in urban India. Through an ethnographic investigation we offer to unpack two questions: 1. Can digital forms of money create financial inclusion by opening up access to the marginalized 2. Can digital platforms amplify socio-economic capacities of low literate users enhancing financial literacy? We argue that digital and financial literacy are an immersive component of digital wallet use acquiring ‘everyday life’ in specific socio-economic ecosystems. Our study captures daily practices of digital money staking a claim in advancing the understanding of financial inclusion as a lived process accumulating habits, practices and stakes to expand socio-economic capabilities.

Abstract

The present disclosure discloses methods and systems for enhancing shopping experience in physical stores. The method includes receiving at least one persona associated with a user based on one or more of: ethnographic data obtained from a user, demographic data associated with the user, buying behavioral data associated with the user, and social networking data associated with the user. After this, one or more historical activities of the user inside one or more physical stores are received. Also, one or more constraints associated with the user are received. Once received, the at least one persona, the one or more constraints, and the one or more historical activities are analyzed to generate a pre-defined number of personalized recommendations. Finally, the personalized recommendations are displayed to the user within a window of a user interface.

Abstract

Understanding people's attitudes towards and uses of technology is an essential aspect of a successful design process. Ethnography is a proven method for acquiring this understanding. However, there are challenges to incorporating fieldwork, most notably the time factor, considered by some as the greatest barrier. This is especially true for many technology companies whose turn-around time from concept to design to implementation is accelerated. We propose a solution to the dilemma between acquiring the benefits of fieldwork with the compressed timescales of many technology projects by using focused field trips as a method for gaining rich insights into peoples' uses of and attitudes towards technologies in real-world settings. In a short amount of time (one or two days), field trips sensitize design teams to the priorities of stakeholder groups. We outline a systematic approach to incorporating field trips as a …

Abstract

Owing to the stigmatization of mental illnesses such as depression in India [1], there is a need for indirect unsuspecting ways to identify risk for depression and provide timely intervention. Healthy-Unhealthy Music scale (HUMS) [2] is one such assessment tool developed on Australian population that uses music engagement as an indicator of anxiety levels and potential high-risk for depression as assessed by Kessler’s Psychological Distress Scale (K10). The current study aims to ascertain its validity in an Indian setting followed by applying machine learning approaches to predict mental well-being from music associations. A diverse group comprising Indian adult population was assessed using HUMS and mental well-being and proneness to depression measures. HUMS structure investigated via Exploratory factor analyses, and concurrent validity tested with correlations to depression risk and wellbeing revealed high external validity and applicability of HUMS in Indian adult population.Furthermore, very low in-sample error for models like Support Vector Machines (SVM) with nonlinear kernels suggests an underlying pattern between HUMS responses and K10 score. Finally, a two-class model resulted in out of sample accuracy of 81%. To conclude, HUMS demonstrates high generalizability and hence applicability in Indian adult population and potential for employing ML models to capture the underlying pattern.

Abstract

Musical training causes structural and functional changes in the brain due to its sensory-motor demands, but the modulatory effect of musical training on music feature processing in the brain in a continuous music listening paradigm, has not been investigated thus far. In this work, we investigate the differences between musicians and non-musicians in the encoding of musical features encompassing musical timbre, rhythm and tone. 18 musicians and 18 non-musicians were scanned using fMRI while listening to 3 varied stimuli. Acoustic features corresponding to timbre, rhythm and tone were computationally extracted from the stimuli and correlated with brain responses, followed by t-tests on group level maps to uncover encoding differences between the two groups. The musicians demonstrated greater involvement of limbic and reward regions, and regions possessing adaptations to music processing due to training, indicating greater analytic processing. However, as a group, they did not exhibit large regions of consistent correlation patterns, especially in processing high-level features, due to differences in processing strategies arising out of their varied training. The non-musicians exhibited broader regions of correlations, implying greater similarities in bottom-up sensory processing.