Abstract

In this work, we address traffic management of multiple payload transport systems comprising of nonholonomic robots. We consider loosely coupled rigid robot formations carrying a payload from one place to another. Each payload transport system (PTS) moves in various kinds of environments with obstacles. We ensure each PTS completes its given task by avoiding collisions with other payload systems and obstacles as well. Each PTS has one leader and multiple followers and the followers maintain a desired distance and angle with respect to the leader using a decentralized leader follower control architecture while moving in the traffic. We showcase, through simulations the time taken by each PTS to traverse its respective trajectory with and without other PTS and obstacles. We show that our strategies help manage the traffic for a large number of PTS moving from one place to another.

Abstract

This paper presents our solution for transportation of deformable payloads using a formation of non-holonomic mobile robots while avoiding static and dynamic obstacles. We assume the center of the formation to be a virtual robot which also plays the role of the leader of the formation. Rapidly exploring Random Tree star (RRT*) algorithm is used to determine a static obstacle free trajectory for the virtual leader. In addition to that, a custom path planning algorithm is incorporated for the follower robots. A decentralized leader-follower formation control is applied for the robots to traverse their respective trajectories without breaking the formation while following the constraints of the deformable payload. The virtual leader moves forward or backward on the initially generated path with variable velocity depending on the proximity of dynamic obstacles. All the other robots follow it so that the formation is able to avoid the dynamic obstacles while still remaining on the path leading towards the destination.

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

This note offers a perspective on the informal markets as an assortment of human players and networks contributing to a socio‐business culture of economic survival. The note also offers a few thoughts on specific segments of the informal workforce in India based on ethnographic evidence and a sociological assessment. The author begins with an appraisal of the socio‐economic phenomenon of informality including an anthropological framing of the “informal.” An important focus is the IT‐based businesses and the variety of players ranging from middle‐range businesses to floating or freelancing actors. The note ends with a spotlight on the internet fuelled gig economy where ICTs move beyond being a product or a service and take over as the business platform ushering a new era of informal or gig economy.

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

Narratives of the gig economy are largely perspectives from the global North and the nature of their ride-hailing market. Freelancing or gigging offer contrasting connotations depending on the market and the labour it is situated in. India, with its demographic dividend and employment challenges, view opportunities arising out of the gig-economy differently. To investigate the impact of ride-hailing apps in the Indian market, and the challenges and opportunities it has brought in, we offer learnings from an ethnographic study of Uber drivers in Indi