Matching Items (12)

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KarateScore

Description

Karate is a Japanese martial art that originated approximately a century ago, with heavy influence from Chinese martial arts at the time. Although it was originally created as a form

Karate is a Japanese martial art that originated approximately a century ago, with heavy influence from Chinese martial arts at the time. Although it was originally created as a form of self-defense, many today practice it for sport. Organizations such as the World Karate Federation (WKF) and USA Karate establish rules for competitions as well as host tournaments for practitioners of all ages and skill levels to participate in. Dojos will often host small, local tournaments for their students to practice and sharpen their competition skills. Smaller tournaments often do not have the same tools and technologies that larger tournaments do. Sign-ups are typically done in-person and payments are cash-only, which can be inconvenient for those who are extremely busy or forgetful. Another issue with hosting local tournaments is that the software used to run the timer is a desktop application, called Karate Semaphore. In the case of technical difficulties, installing the software on another machine can be extremely time-consuming and delay the progression of the tournament. Not to mention, Karate Semaphore was created following the 2012 WKF rules—meaning it is currently out of date, as it does not contain any features supporting new rules.
For my creative project, I designed a website through which smaller, local tournament registration and management are possible. Users can register for tournaments through the registration page. Registered users can check their registration is successful by viewing a table of all competitors. If the list of competitors is too long, they can filter results based on search criteria. Tournament management will be possible via a functioning timer following WKF rules which keeps track of both the match’s score as well as time.

Contributors

Agent

Created

Date Created
  • 2019-05

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Composition of Geographic-Based Component Simulation Models

Description

Component simulation models, such as agent-based models, may depend on spatial data associated with geographic locations. Composition of such models can be achieved using a Geographic Knowledge Interchange Broker (GeoKIB)

Component simulation models, such as agent-based models, may depend on spatial data associated with geographic locations. Composition of such models can be achieved using a Geographic Knowledge Interchange Broker (GeoKIB) enabled with spatial-temporal data transformation functions, each of which is responsible for a set of interactions between two independent models. The use of autonomous interaction models allows model composition without alteration of the composed component models. An interaction model must handle differences in the spatial resolutions between models, in addition to differences in their temporal input/output data types and resolutions.

A generalized GeoKIB was designed that regulates unidirectional spatially-based interactions between composed models. Different input and output data types are used for the interaction model, depending on whether data transfer should be passive or active. Synchronization of time-tagged input/output values is made possible with the use of dependency on a discrete simulation clock. An algorithm supporting spatial conversion is developed to transform any two-dimensional geographic data map between different region specifications. Maps belonging to the composed models can have different regions, map cell sizes, or boundaries. The GeoKIB can be extended based on the model specifications to be composed and the target application domain.

Two separate, simplistic models were created to demonstrate model composition via the GeoKIB. An interaction model was created for each of the two directions the composed models interact. This exemplar is developed to demonstrate composition and simulation of geographic-based component models.

Contributors

Agent

Created

Date Created
  • 2019

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Feature Extraction from Multi-variate Time Series and Resource-Aware Indexing

Description

In the presence of big data analysis, large volume of data needs to be systematically indexed to support analytical tasks, such as feature engineering, pattern recognition, data mining, and query

In the presence of big data analysis, large volume of data needs to be systematically indexed to support analytical tasks, such as feature engineering, pattern recognition, data mining, and query processing. The volume, variety, and velocity of these data necessitate sophisticated systems to help researchers understand, analyze, and dis- cover insights from heterogeneous, multidimensional data sources. Many analytical frameworks have been proposed in the literature in recent years, but challenges to accuracy, speed, and effectiveness remain hence a systematic approach to perform data signature computation and query processing in multi-dimensional space is in people’s interest. In particular, real-time and near real-time queries pose significant challenges when working with large data sets.

To address these challenges, I develop an innovative robust multi-variate fea- ture extraction algorithm over multi-dimensional temporal datasets, which is able to help understand and analyze various real-world applications. Furthermore, to an- swer queries over these features, I develop a novel resource-aware indexing framework to approximately solve top-k queries by leveraging onion-layer indexing in conjunc- tion with locality sensitive hashing. The proposed indexing scheme allows people to answer top-k queries by only accessing a bounded amount of data, which optimizes big data small for queries.

Contributors

Agent

Created

Date Created
  • 2020

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Locality sensitive indexing for efficient high-dimensional query answering in the presence of excluded regions

Description

Similarity search in high-dimensional spaces is popular for applications like image

processing, time series, and genome data. In higher dimensions, the phenomenon of

curse of dimensionality kills the effectiveness of most of

Similarity search in high-dimensional spaces is popular for applications like image

processing, time series, and genome data. In higher dimensions, the phenomenon of

curse of dimensionality kills the effectiveness of most of the index structures, giving

way to approximate methods like Locality Sensitive Hashing (LSH), to answer similarity

searches. In addition to range searches and k-nearest neighbor searches, there

is a need to answer negative queries formed by excluded regions, in high-dimensional

data. Though there have been a slew of variants of LSH to improve efficiency, reduce

storage, and provide better accuracies, none of the techniques are capable of

answering queries in the presence of excluded regions.

This thesis provides a novel approach to handle such negative queries. This is

achieved by creating a prefix based hierarchical index structure. First, the higher

dimensional space is projected to a lower dimension space. Then, a one-dimensional

ordering is developed, while retaining the hierarchical traits. The algorithm intelligently

prunes the irrelevant candidates while answering queries in the presence of

excluded regions. While naive LSH would need to filter out the negative query results

from the main results, the new algorithm minimizes the need to fetch the redundant

results in the first place. Experiment results show that this reduces post-processing

cost thereby reducing the query processing time.

Contributors

Agent

Created

Date Created
  • 2016

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A Comparative Study on the Performance Isolation of Virtualization Technologies

Description

Virtualization technologies are widely used in modern computing systems to deliver shared resources to heterogeneous applications. Virtual Machines (VMs) are the basic building blocks for Infrastructure as a Service (IaaS),

Virtualization technologies are widely used in modern computing systems to deliver shared resources to heterogeneous applications. Virtual Machines (VMs) are the basic building blocks for Infrastructure as a Service (IaaS), and containers are widely used to provide Platform as a Service (PaaS). Although it is generally believed that containers have less overhead than VMs, an important tradeoff which has not been thoroughly studied is the effectiveness of performance isolation, i.e., to what extent the virtualization technology prevents the applications from affecting each other’s performance when they share the resources using separate VMs or containers. Such isolation is critical to provide performance guarantees for applications consolidated using VMs or containers. This paper provides a comprehensive study on the performance isolation for three widely used virtualization technologies, full virtualization, para-virtualization, and operating system level virtualization, using Kernel-based Virtual Machine (KVM), Xen, and Docker containers as the representative implementations of these technologies. The results show that containers generally have less performance loss (up to 69% and 41% compared to KVM and Xen in network latency experiments, respectively) and better scalability (up to 83.3% and 64.6% faster compared to KVM and Xen when increasing number of VMs/containers to 64, respectively), but they also suffer from much worse isolation (up to 111.8% and 104.92% slowdown compared to KVM and Xen when adding disk stress test in TeraSort experiments under full usage (FU) scenario, respectively). The resource reservation tools help virtualization technologies achieve better performance (up to 85.9% better disk performance in TeraSort under FU scenario), but cannot help them avoid all impacts.

Contributors

Agent

Created

Date Created
  • 2019

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SearchViz: an interactive visual interface to navigate search-results in online discussion forums

Description

Online programming communities are widely used by programmers for troubleshooting or various problem solving tasks. Large and ever increasing volume of posts on these communities demands more efforts to read

Online programming communities are widely used by programmers for troubleshooting or various problem solving tasks. Large and ever increasing volume of posts on these communities demands more efforts to read and comprehend thus making it harder to find relevant information. In my thesis; I designed and studied an alternate approach by using interactive network visualization to represent relevant search results for online programming discussion forums.

I conducted user study to evaluate the effectiveness of this approach. Results show that users were able to identify relevant information more precisely via visual interface as compared to traditional list based approach. Network visualization demonstrated effective search-result navigation support to facilitate user’s tasks and improved query quality for successive queries. Subjective evaluation also showed that visualizing search results conveys more semantic information in efficient manner and makes searching more effective.

Contributors

Agent

Created

Date Created
  • 2015

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Space adaptation techniques for preference oriented skyline processing

Description

Skyline queries are a well-established technique used in multi criteria decision applications. There is a recent interest among the research community to efficiently compute skylines but the problem of presenting

Skyline queries are a well-established technique used in multi criteria decision applications. There is a recent interest among the research community to efficiently compute skylines but the problem of presenting the skyline that takes into account the preferences of the user is still open. Each user has varying interests towards each attribute and hence "one size fits all" methodology might not satisfy all the users. True user satisfaction can be obtained only when the skyline is tailored specifically for each user based on his preferences.

This research investigates the problem of preference aware skyline processing which consists of inferring the preferences of users and computing a skyline specific to that user, taking into account his preferences. This research proposes a model that transforms the data from a given space to a user preferential space where each attribute represents the preference of the user. This study proposes two techniques "Preferential Skyline Processing" and "Latent Skyline Processing" to efficiently compute preference aware skylines in the user preferential space. Finally, through extensive experiments and performance analysis the correctness of the recommendations and the algorithm's ability to outperform the naïve ones is confirmed.

Contributors

Agent

Created

Date Created
  • 2014

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Multiobjective Optimization Based Approach for Truth Discovery

Description

There are many applications where the truth is unknown. The truth values are

guessed by different sources. The values of different properties can be obtained from

various sources. These will lead to

There are many applications where the truth is unknown. The truth values are

guessed by different sources. The values of different properties can be obtained from

various sources. These will lead to the disagreement in sources. An important task

is to obtain the truth from these sometimes contradictory sources. In the extension

of computing the truth, the reliability of sources needs to be computed. There are

models which compute the precision values. In those earlier models Banerjee et al.

(2005) Dong and Naumann (2009) Kasneci et al. (2011) Li et al. (2012) Marian and

Wu (2011) Zhao and Han (2012) Zhao et al. (2012), multiple properties are modeled

individually. In one of the existing works, the heterogeneous properties are modeled in

a joined way. In that work, the framework i.e. Conflict Resolution on Heterogeneous

Data (CRH) framework is based on the single objective optimization. Due to the

single objective optimization and non-convex optimization problem, only one local

optimal solution is found. As this is a non-convex optimization problem, the optimal

point depends upon the initial point. This single objective optimization problem is

converted into a multi-objective optimization problem. Due to the multi-objective

optimization problem, the Pareto optimal points are computed. In an extension of

that, the single objective optimization problem is solved with numerous initial points.

The above two approaches are used for finding the solution better than the solution

obtained in the CRH with median as the initial point for the continuous variables and

majority voting as the initial point for the categorical variables. In the experiments,

the solution, coming from the CRH, lies in the Pareto optimal points of the multiobjective

optimization and the solution coming from the CRH is the optimum solution

in these experiments.

Contributors

Agent

Created

Date Created
  • 2019

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A Framework for Interactive Geospatial Map Cleaning using GPS Trajectories

Description

A volunteered geographic information system, e.g., OpenStreetMap (OSM), collects data from volunteers to generate geospatial maps. To keep the map consistent, volunteers are expected to perform the tedious task of

A volunteered geographic information system, e.g., OpenStreetMap (OSM), collects data from volunteers to generate geospatial maps. To keep the map consistent, volunteers are expected to perform the tedious task of updating the underlying geospatial data at regular intervals. Such a map curation step takes time and considerable human effort. In this thesis, we propose a framework that improves the process of updating geospatial maps by automatically identifying road changes from user-generated GPS traces. Since GPS traces can be sparse and noisy, the proposed framework validates the map changes with the users before propagating them to a publishable version of the map. The proposed framework achieves up to four times faster map matching performance than the state-of-the-art algorithms with only 0.1-0.3% accuracy loss.

Contributors

Agent

Created

Date Created
  • 2017

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SPSR efficient processing of socially k-nearest neighbors with spatial range filter

Description

Social media has become popular in the past decade. Facebook for example has 1.59 billion active users monthly. With such massive social networks generating lot of data, everyone is constantly

Social media has become popular in the past decade. Facebook for example has 1.59 billion active users monthly. With such massive social networks generating lot of data, everyone is constantly looking for ways of leveraging the knowledge from social networks to make their systems more personalized to their end users. And with rapid increase in the usage of mobile phones and wearables, social media data is being tied to spatial networks. This research document proposes an efficient technique that answers socially k-Nearest Neighbors with Spatial Range Filter. The proposed approach performs a joint search on both the social and spatial domains which radically improves the performance compared to straight forward solutions. The research document proposes a novel index that combines social and spatial indexes. In other words, graph data is stored in an organized manner to filter it based on spatial (region of interest) and social constraints (top-k closest vertices) at query time. That leads to pruning necessary paths during the social graph traversal procedure, and only returns the top-K social close venues. The research document then experimentally proves how the proposed approach outperforms existing baseline approaches by at least three times and also compare how each of our algorithms perform under various conditions on a real geo-social dataset extracted from Yelp.

Contributors

Agent

Created

Date Created
  • 2016