Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Subject "System"

Now showing 1 - 10 of 10
  • No Thumbnail Available
    Item
  • Thumbnail Image
    Item
    Database management system support for collaborative filtering recommender systems
    (2014-08) Sarwat, Mohamed
    Recommender systems help users identify useful, interesting items or content (data)from a considerably large search space. By far, the most popular recommendation technique used is collaborative filtering which exploits the users' opinions (e.g., movie ratings) and/or purchasing (e.g., watching, reading) history in order to extract a set of interesting items for each user. Database Management Systems (DBMSs) do not provide in-house support for recommendation applications despite their popularity. Existing recommender system architectures either do not employ a DBMS at all or only uses it as a data store whereas the recommendation logic is implemented in-full outside the database engine. Incorporating the recommendation functionality inside the DBMS kernel is beneficial for the following reasons: (1) Many recommendation algorithms take as input structured data (users, items, and user historical preferences) that could be adequately stored and accessed using a database system. (2) The In-DBMS approach facilitates applying the recommendation functionality and typical database operations(e.g., Selection, Join) side-by-side. That allows application developers to go beyond traditional recommendation applications, e.g., "Recommend to Alice ten movies", and flexibly define Arbitrary Recommendation scenarios like "Recommend ten nearby restaurants to Alice" and "Recommend to Bob ten movies watched by her friends". (3) Once the recommendation functionality lives inside the database kernel, the recommendation application takes advantage of the DMBS inherent features (e.g., query optimization, materialized views, indexing) provided by the storage manager and query execution engine.This thesis studies the incorporation of the recommendation functionality inside the core engine of a database management system. This is a major departure from existing recommender system architectures that are implemented on-top of a database engines using either SQL queries or stored procedures. The on-top approach does not harness the full power of the database engine (i.e., query execution engine, storage manager)since it always generates recommendations first and then performs other database operations. Ideas developed in this thesis are implemented inside RecDB ; an opensource recommendation engine built entirely inside PostgreSQL (open source relational database system).
  • No Thumbnail Available
    Item
    Fenrir Flight 04
    (2014-09-02) Taylor, Brian
  • No Thumbnail Available
    Item
    Fenrir Flight 05
    (2014-09-02) Taylor, Brian
  • No Thumbnail Available
    Item
    Fenrir Flight 06
    (2014-09-02) Taylor, Brian
  • No Thumbnail Available
    Item
    Fenrir Flight 07
    (2014-09-02) Taylor, Brian
  • No Thumbnail Available
    Item
    Fenrir Flight 08
    (2014-09-02) Taylor, Brian
  • No Thumbnail Available
    Item
    Goldy Flight Control System v1.0
    (2014-07-23) Johnson, Will
  • Thumbnail Image
    Item
    Privacy preserving performance enhancements for anonymous communication networks
    (2012-10) Jansen, Robert G.
    An anonymous communication system hides the fact that two parties are communicating, and as a result, drastically improves the online privacy of those using it. Tor is the most popular anonymous communication system deployed, but its popularity has illuminated problems with its design that have made it unbearably slow for many users who would otherwise benefit from its protections. These performance problems have been recognized, but there has been little work on designing and properly evaluating practical solutions that improve performance while also preserving privacy. We initiate an exploration into Tor's system design and the quality of the communication it provides. First, we design and develop a simulation tool, called Shadow, that allows us to experiment with the Tor software in a safe but realistic and controllable manner. We then give a precise model of the Tor network, the backbone networks upon which it operates, and the user agents operating within it. We show that by combining our model with Shadow, our experimentation environment is capable of producing network interactions and performance qualities indicative of real systems. We then investigate performance enhancements in three major areas of Tor's design. We explore Tor's utilization of resources by evaluating both existing and new circuit scheduling techniques, and show the extent to which scheduling can be used to prioritize traffic in order to improve desirable quality metrics. We then design and evaluate algorithms focused on reducing network load by throttling agents that consume an unfair share of network resources. Finally, in an effort to supplement Tor's volunteered resources, we design and analyze two schemes that increase network capacity by providing incentives to those contributing resources to the system.
  • No Thumbnail Available
    Item
    Python Attitude Heading and Reference System
    (2014-07-23) Taylor, Brian