Computer Science
http://hdl.handle.net/10139/772
2024-03-29T09:34:01ZWhirlpool: A microservice style scalable continuous topical web crawler
http://hdl.handle.net/10211.3/214919
Whirlpool: A microservice style scalable continuous topical web crawler
Pereira, Rihan Stephen
Historically, web crawlers/bots/spiders have been well known for indexing, ranking websites on the internet. This thesis augments the crawling activity but approaches the problem through the lens of a data engineer. Whirlpool as a continuous, topical web crawling tool is also a data ingestion pipeline implemented from bottom-up using RabbitMQ which is a high performance messaging buffer to organize the data flow within its network. It is based on a open, standard blueprint design of mercator. This paper discusses the high and low level design of this complex program covering auxiliary data structures, object-oriented design, addressing scalability concerns, and deployment on AWS. The project name Whirlpool is used as an analogy referring to the naturally occurring phenomenon where opposing water currents in sea cause water to spin round and round drawing various objects into it.
2019-12-01T00:00:00ZVoting Systems: From Method to Algorithm
http://hdl.handle.net/10211.3/214761
Voting Systems: From Method to Algorithm
Devlin, Christopher R.
Voting and choice aggregation are used widely not just in politics but in business decision making processes and other areas such as competitive bidding procurement. Stakeholders and others who rely on these systems require them to be fast, efficient, and, most importantly, fair. The focus of this thesis is to illustrate the complexities inherent in voting systems. The algorithms intrinsic in several voting systems are made explicit as a way to simplify choices among these systems. The systematic evaluation of the algorithms associated with choice aggregation will provide a groundwork for future research and the implementation of these tools across public and private spheres.
2019-12-01T00:00:00ZHelping Sapiens: A Geo-based mobile Application to help people in the vicinity
http://hdl.handle.net/10211.3/214585
Helping Sapiens: A Geo-based mobile Application to help people in the vicinity
Singh, Simrandeep
2019-12-01T00:00:00ZPath Planning And Collision Avoidance for Clustered Central Place Foraging
http://hdl.handle.net/10211.3/214578
Path Planning And Collision Avoidance for Clustered Central Place Foraging
Bharaswadkar, Apurva
Central place foraging algorithms for multiple robots are gaining attention due to their performance and efficiency in various applications like planetary surveys, mining, object transportation and manipulation. In foraging tasks, multiple robots search for resources and deposit the collected resources to a particular location called “nest” or “home”. If the resources are deposited at a central single collection point, it becomes a central place foraging task. The performance of central place foraging approaches is reduced due to reactive interrobot collision avoidance. The performance decreases in two cases, first case is when two or more robots collect the resources from the same cluster and go to the central location for deposition and the second case is when the path of one robot going to nest from its search position or vice versa intersects with the path of another robot searching for resources. The approach proposed in this thesis is called Path Planning And Collision Avoidance Algorithm For Clustered Central Place Foraging (PPCA-CCPFA). PPCA-CCPFA concentrates on improving the performance of central place foraging task in terms of reducing the number of inter robot collisions and improving target collection in given time for clustered resource distributions. We compare our approach to the popular Distributed Deterministic Spiral Algorithm (DDSA). The proposed algorithm detects inter robot collision and finds an alternate collision free path for a robot in case 1 and adds a delay time for a robot in case 2. This approach has shown notable increase in the performance of DDSA with a single 8 x 8 resource cluster. This algorithm is tested on a single cluster resource distribution at random locations in the arena for a swarm size of 3 to 15 robots.
2019-12-04T00:00:00Z