{"id":1290,"date":"2024-12-24T10:27:09","date_gmt":"2024-12-24T15:27:09","guid":{"rendered":"https:\/\/www.golive.clarku.edu\/faculty\/profiles\/olufemi-odegbile\/"},"modified":"2026-04-05T22:08:28","modified_gmt":"2026-04-06T02:08:28","slug":"olufemi-odegbile","status":"publish","type":"cu_faculty","link":"https:\/\/www.clarku.edu\/faculty\/profiles\/olufemi-odegbile\/","title":{"rendered":"Olufemi Odegbile"},"content":{"rendered":"","protected":false},"author":0,"featured_media":1933,"parent":0,"template":"","meta":{"cu_faculty_f180_userid":"C70292037","cu_faculty_first_name":"Olufemi","cu_faculty_last_name":"Odegbile","cu_faculty_employment_status":"Full Time","cu_faculty_rank":"Assistant Professor","cu_faculty_position":"Assistant Professor","cu_faculty_phone":"","cu_faculty_email":"OOdegbile@clarku.edu","cu_faculty_location":"","cu_faculty_about":"","cu_faculty_degrees":"","cu_faculty_cv":"https:\/\/faculty180.interfolio.com\/public\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU1zT21qREtJZEdWZ0JtU2c2QWVNbGM1VHQ5S0daTGhzckQ1L2FSU0hxZjR0TmY5VWYvcVJJdXFFVFQyOThDSllKd1Z3bDkvR0NkYjk%3D","cu_faculty_links":"{\"ORCID ID\":\"https:\\\/\\\/dblp.org\\\/pid\\\/222\\\/5461.html\"}","cu_faculty_scholarly_interests":"","cu_faculty_scholarly_works":"[{\"activityid\":10933,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"From CountMin to Super kJoin Sketches for Flow Spread Estimation\",\"Journal Title\":\"IEEE Trans. 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For example, the proposed streaming algorithms will be able to identify malicious intrusions and anomalies in an enterprise network and at the same time rank the popularity of contents (social media) and products (e-commerce) to provide important guidance for business decisions. Real-world data streaming applications in large data centers must deal with heterogeneous, and often limited, resource availability. The algorithms explored in this work are designed to be so lightweight that they can monitor and secure a network without interfering with other applications. In other words, the algorithms will be fast and have a small memory footprint without compromising the networks\\u2019 usability. The flexibility of this work extends beyond multiple tasks. Users or applications will also have many options for multi-dimensional tradeoffs among performance, overhead, resource usage, and accuracy. 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Instead of focusing on individual streaming algorithms to meet specific measurement requirements, this project takes a different research path with a holistic, systematic approach that explores the fundamental design principles and establish several common frameworks, each for a family of streaming algorithms that share the same implementation structure, providing a high level of adaptability and many options of multi-dimensional tradeoff among performance, overhead, resource, and accuracy. &lt;\\\/span&gt;&lt;\\\/p&gt;\\n&lt;p&gt;&lt;span&gt;Our research plan consists of three thrusts. First, we generalize the underlying structure of a Bloom filter to a baseline k-min streaming family by replacing bits with other data structures. We then introduce &lt;\\\/span&gt;&lt;span&gt;k&lt;\\\/span&gt;&lt;span&gt;Join super &lt;\\\/span&gt;&lt;span&gt;k&lt;\\\/span&gt;&lt;span&gt;Join families with new streaming structures to achieve both measurement accuracy and memory efficiency. 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