Scalable Temporal Queries on User-Generated Data

With the proliferation of user-generated data, many emerging applications consume this data to serve various important domains, such as natural disaster management, citizen journalism, social recommendations, targeted advertising, and scientific research. This data mostly comes in streaming fashion with tremendous high rates and adds up to large archives of historical data. This dissertation studies indexing and querying this data in different contexts in order to support low latency queries. First, we evaluate ten different indexes that support spatial-keyword queries on streaming data at the system level. These queries, namely range query and $k$-nearest neighbors, are extended to include the time dimension in addition to the space and keywords to effectively serve streaming spatial data applications. Supporting such queries on streaming environment is challenging as streaming data comes in a very high rate, and query answer is likely changing around the clock. The extensive evaluation provides insights for the system builders on the potential loss and gain of employing one index over the others from the system perspectives. Second, we introduce two new spatial-temporal personalized queries that tailor the results to the query issuer based on the user’s social network. In addition, we propose a scalable geo-social indexing framework which digests real-time geo-social data. The framework distinguishes highly-dynamic data from relatively stable data and uses appropriate data structures and storage tier for each. The query processor utilizes this framework to support real-time query response and minimal overhead on the system resources by employing different types of pruning. Lastly, we study community-centric queries on user-generated data that capture the community interests over time. Understanding a specific community is very crucial in making decisions and writing policies. A novel indexing paradigm is proposed to efficiently digest the community interactions. Furthermore, we develop scalable techniques, exact and approximate, to find the top-k that a specific community interacted the most during a given time. The proposed techniques smartly prune the search space to provide a low query latency

Scalable Temporal Queries on User-Generated Data

With the proliferation of user-generated data, many emerging applications consume this data to serve various important domains, such as natural disaster management, citizen journalism, social recommendations, targeted advertising, and scientific research. This data mostly comes in streaming fashion with tremendous high rates and adds up to large archives of historical data. This dissertation studies indexing and querying this data in different contexts in order to support low latency queries. First, we evaluate ten different indexes that support spatial-keyword queries on streaming data at the system level. These queries, namely range query and $k$-nearest neighbors, are extended to include the time dimension in addition to the space and keywords to effectively serve streaming spatial data applications. Supporting such queries on streaming environment is challenging as streaming data comes in a very high rate, and query answer is likely changing around the clock. The extensive evaluation provides insights for the system builders on the potential loss and gain of employing one index over the others from the system perspectives. Second, we introduce two new spatial-temporal personalized queries that tailor the results to the query issuer based on the user’s social network. In addition, we propose a scalable geo-social indexing framework which digests real-time geo-social data. The framework distinguishes highly-dynamic data from relatively stable data and uses appropriate data structures and storage tier for each. The query processor utilizes this framework to support real-time query response and minimal overhead on the system resources by employing different types of pruning. Lastly, we study community-centric queries on user-generated data that capture the community interests over time. Understanding a specific community is very crucial in making decisions and writing policies. A novel indexing paradigm is proposed to efficiently digest the community interactions. Furthermore, we develop scalable techniques, exact and approximate, to find the top-k that a specific community interacted the most during a given time. The proposed techniques smartly prune the search space to provide a low query latency

SPIN: A Blockchain-Based Framework for Sharing COVID-19 Pandemic Information across Nations

The COVID-19 pandemic has caused many countries around the globe to put strict policies and measures in place in an attempt to control the rapid spread of the virus. These measures have affected economic activities and have impacted a broad range of businesses, such as international traveling, restaurants, and shopping malls. As COVID-19 vaccination efforts progress, countries are starting to relax international travel constraints and permit passengers from certain destinations to cross the border. Moreover, travelers from those destinations are likely required to provide certificates of vaccination results or negative COVID-19 tests before crossing the borders. Implementing these travel guidelines requires sharing information between countries, such as the number of COVID-19 cases and vaccination certificates for travelers. In this paper, we introduce SPIN, a framework leveraging a permissioned blockchain for sharing COVID-19 information between countries. This includes public data, such as the number of vaccinated people, and private data, such as vaccination certificates for individuals. Additionally, we employ cancelable fingerprint templates to authenticate private information about travelers. We analyze the framework from scalability, efficiency, security, and privacy perspectives. To validate our framework, we provide a prototype implementation using the Hyperledger Fabric platform

Managing Expatriate Employment Contracts with Blockchain

Expatriates, or migrant workers, are employees who work outside their home country and reside in a foreign country for the purpose of work. They are often subject to job fraud, employment contract violations, and poor working conditions. These calamities are mainly due to language barriers, limited legal protection, and feeling inferior in their host countries. Many reports have indicated that minimum working and living standards for expatriates are not as adequately enforced as those for domestic employees. These issues may be elevated with the presence of an employment contract framework, which would enable better enforcement and wider visibility for both workers and employers. Thus, we propose a blockchain-powered framework to represent expatriate employment contracts as digital assets managed by smart contracts. It enables employers to create contracts to which employees agree in a decentralized, tamper-proof, transparent, and traceable manner. This framework facilitates auditability, tracking, and enhanced visibility of expatriate employment contracts and job history verification for both workers and employers. We provide a prototype implementation using the Hyperledger Fabric platform and analyze the framework qualitatively from scalability, efficiency, security, and privacy perspectives