Shriram Ramesh

Bio: Shriram Ramesh is an academic researcher from Indian Institute of Science. The author has contributed to research in topics: Graph database & Query plan. The author has an hindex of 2, co-authored 6 publications receiving 11 citations.

GoCoronaGo: Privacy Respecting Contact Tracing for COVID-19 Management.

Abstract: The COVID-19 pandemic is imposing enormous global challenges in managing the spread of the virus. A key pillar to mitigation is contact tracing, which complements testing and isolation. Digital apps for contact tracing using Bluetooth technology available in smartphones have gained prevalence globally. In this article, we discuss various capabilities of such digital contact tracing, and its implication on community safety and individual privacy, among others. We further describe the GoCoronaGo institutional contact tracing app that we have developed, and the conscious and sometimes contrarian design choices we have made. We offer a detailed overview of the app, backend platform and analytics, and our early experiences with deploying the app to over 1000 users within the Indian Institute of Science campus in Bangalore. We also highlight research opportunities and open challenges for digital contact tracing and analytics over temporal networks constructed from them.

A Distributed Path Query Engine for Temporal Property Graphs

Abstract: Property graphs are a common form of linked data, with path queries used to traverse and explore them for enterprise transactions and mining. Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and their properties and structure vary over time. These are seen in social, telecom and transit networks. However, current graph databases and query engines have limited support for temporal relations among graph entities, no support for time-varying entities and/or do not scale on distributed resources. We address this gap by extending a linear path query model over property graphs to include intuitive temporal predicates that operate over temporal graphs. We design a distributed execution model for these temporal path queries using the interval-centric computing model, and develop a novel cost model to select an efficient execution plan from several. We perform detailed experiments of our $\mathcal{G}ranite$ distributed query engine using temporal property graphs as large as 52M vertices, 218M edges and 118M properties, and an 800-query workload, derived from the LDBC benchmark. We offer sub-second query latencies in most cases, which is 149×-1140× faster compared to industry-leading Neo4J shared- memory graph database and the JanusGraph/Spark distributed graph query engine. Further, our cost model selects a query plan that is within 10% of the optimal execution time in 90% of the cases. We also scale well, and complete 100% of the queries for all graphs, compared to only 32-92% by baseline systems.

Optimal Cost Truck Triangulation in a Dynamic Intermodal Freight Transportation Supply Chain

Abstract: Management of containers and carriers in a supply chain that spreads across different intermodal legs of ocean, land, river, rail and air transport is a challenging task in the shipping industry. During the intermodal phase, the triangulation of containers or carriers is a process that is sought to minimize cost by saving a possible transport leg. In this paper, we discuss an optimal triangulation process of containers carried by trucks in an intermodal transport network. We are addressing a specific triangulation process for the trucks engaged in import drops or export pickups of containers such that they can be effectively reused for the next export pickups or import drops in locations within a neighbourhood. We propose a mathematical model to address this problem in the framework of minimum cost network flows. Further, we introduce a heuristic method using the successive shortest path algorithm for the proposed model. The model is analyzed using data from current shipping networks of one of the major shipping industries for its North America database.

Resilient Execution of Data-triggered Applications on Edge, Fog and Cloud Resources

Abstract: Internet of Things (loT) is leading to the pervasive availability of streaming data about the physical world, coupled with edge computing infrastructure deployed as part of smart cities and 5G rollout. These constrained, less reliable but cheap resources are complemented by fog resources that offer feder-ated management and accelerated computing, and pay-as-you-go cloud resources. There is a lack of intuitive means to deploy application pipelines to consume such diverse streams, and to execute them reliably on edge and fog resources. We propose an innovative application model to declaratively specify queries to match streams of micro-batch data from stream sources and trigger the distributed execution of data pipelines. We also design a resilient scheduling strategy using advanced reservation on reliable fogs to guarantee dataflow completion within a deadline while minimizing the execution cost. Our detailed experiments on over 100 virtual loT resources and for $\approx 10k$ task executions, with comparison against baseline scheduling strategies, illustrates the cost-effectiveness, resilience and scalability of our framework.

The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review.

Abstract: The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies.

A Survey on Security and Privacy Issues in Contact Tracing Application of Covid-19

Abstract: In response to the coronavirus (COVID-19) pandemic, Government and public health authorities around the world are developing contact tracing apps as a way to trace and slow the unfold of the virus. There is major divergence among nations, however, between a "privacy-first" approach that protects citizens' information at the price of very restricted access for public health authorities and a "data-first" approach that stores massive amounts of knowledge that, whereas of immeasurable price to epidemiologists. Contact tracing apps work by gathering information from people who have tested positive for the virus and so locating and notifying individuals with whom those people are in shut contact, oftentimes by use of GPS, Bluetooth, or wireless technology. All of the user's information is employed and picked up, the study found that users' information would be created anonymous, encrypted, secured, and can be transmitted on-line and stored solely in an aggregated format. Contact tracing apps use either a centralized or a decentralized approach to work the user's information. Apps that use a centralized approach have high privacy risks. In this paper, the researcher's contributions related to the security and privacy of Contact tracing apps have been discussed and, later research gaps have been identified with proposed solutions.

The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19

Abstract: With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.

Wearable devices and IoT applications for symptom detection, infection tracking, and diffusion containment of the COVID-19 pandemic: a survey

Abstract: Until a safe and effective vaccine to fight the SARS-CoV-2 virus is developed and available for the global population, preventive measures, such as wearable tracking and monitoring systems supported by Internet of Things (IoT) infrastructures, are valuable tools for containing the pandemic. In this review paper we analyze innovative wearable systems for limiting the virus spread, early detection of the first symptoms of the coronavirus disease COVID-19 infection, and remote monitoring of the health conditions of infected patients during the quarantine. The attention is focused on systems allowing quick user screening through ready-to-use hardware and software components. Such sensor-based systems monitor the principal vital signs, detect symptoms related to COVID-19 early, and alert patients and medical staff. Novel wearable devices for complying with social distancing rules and limiting interpersonal contagion (such as smart masks) are investigated and analyzed. In addition, an overview of implantable devices for monitoring the effects of COVID-19 on the cardiovascular system is presented. Then we report an overview of tracing strategies and technologies for containing the COVID-19 pandemic based on IoT technologies, wearable devices, and cloud computing. In detail, we demonstrate the potential of radio frequency based signal technology, including Bluetooth Low Energy (BLE), Wi-Fi, and radio frequency identification (RFID), often combined with Apps and cloud technology. Finally, critical analysis and comparisons of the different discussed solutions are presented, highlighting their potential and providing new insights for developing innovative tools for facing future pandemics.

Supporting Dynamic Graphs and Temporal Entity Deletions in the LDBC Social Network Benchmark's Data Generator

Abstract: Many data processing pipelines operate on highly-connected data sets that can be efficiently modelled as graphs. These graphs are rarely static, but rather change rapidly and often exhibit dynamic, temporal, or streaming behaviour. During the last decade, numerous graph benchmarks have been proposed, which cover a significant portion of the features required in practical use cases. However, whilst these benchmarks often contain some update operations, none of them include complex deletions, which makes it challenging to test the performance of graph processing systems under such operations. To address this limitation, we have extended the LDBC Social Network Benchmark (SNB) by introducing lifespan attributes for the creation and deletion dates of its entities. We have defined constraints for selecting these dates from intervals that ensure that the graph always satisfies the cardinality constraints prescribed by the schema and other semantic constraints of the social network domain. We have implemented the proposed lifespans in the SNB generator.