Big data analytics is gaining popularity for enterprises in optimizing their business processes ranging from retailers, supply chains, to online shopping stores. Existing practical raw data are far from usable to achieve the goal. Therefore, a good data pre-processing approach is required and is a key step to success. We propose to research on the effectiveness of data pre-processing and the business process based on a real world database. Our methodology involves natural language processing. Our key goal is to study appropriate methods with big data analysis techniques that can handle errors, ambiguity, and repeated descriptions caused by human languages. In this study, we did a simple language similarity checking to understand the database status. We also applied a logical representation system in our database to prove this concept.
","abstract_html":"<p>Big data analytics is gaining popularity for enterprises in optimizing their business processes ranging from retailers, supply chains, to online shopping stores. Existing practical raw data are far from usable to achieve the goal. Therefore, a good data pre-processing approach is required and is a key step to success. We propose to research on the effectiveness of data pre-processing and the business process based on a real world database. Our methodology involves natural language processing. Our key goal is to study appropriate methods with big data analysis techniques that can handle errors, ambiguity, and repeated descriptions caused by human languages. In this study, we did a simple language similarity checking to understand the database status. We also applied a logical representation system in our database to prove this concept.</p>","abstract_has_math":false,"creators":["Lu, Po-Chun"],"institution":null,"degree_name":"Master of Science in Computer Science (MSCS)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Dr. Dan Lo","Dr. Reza Meimandi Parizi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-07-20T07:00:00Z","date_published":"2021-07-20T07:00:00Z","updated_at":"2026-07-24T02:43:51Z","subjects":["Big Data","Database","Logical representation","Computer Sciences","Data Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/cs_etd/48","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Dan Lo","Dr. Reza Meimandi Parizi"]},{"key":"dc:creator","label":"Author","values":["Lu, Po-Chun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2022-12-31T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Science (MSCS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Big Data","Database","Logical representation","Computer Sciences","Data Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/cs_etd/48"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Big data analytics is gaining popularity for enterprises in optimizing their business processes ranging from retailers, supply chains, to online shopping stores. Existing practical raw data are far from usable to achieve the goal. Therefore, a good data pre-processing approach is required and is a key step to success. We propose to research on the effectiveness of data pre-processing and the business process based on a real world database. Our methodology involves natural language processing. Our key goal is to study appropriate methods with big data analysis techniques that can handle errors, ambiguity, and repeated descriptions caused by human languages. In this study, we did a simple language similarity checking to understand the database status. We also applied a logical representation system in our database to prove this concept.
"]},{"key":"dc:title","label":"Title","values":["Using Big Data Analytics to Optimize Practical Large Databases"]}]}],"canonical_facts":{"dc:contributor":["Dr. Dan Lo","Dr. Reza Meimandi Parizi"],"dc:creator":["Lu, Po-Chun"],"dc:date.available":["2022-12-31T08:00:00Z"],"dc:description.abstract":["Big data analytics is gaining popularity for enterprises in optimizing their business processes ranging from retailers, supply chains, to online shopping stores. Existing practical raw data are far from usable to achieve the goal. Therefore, a good data pre-processing approach is required and is a key step to success. We propose to research on the effectiveness of data pre-processing and the business process based on a real world database. Our methodology involves natural language processing. Our key goal is to study appropriate methods with big data analysis techniques that can handle errors, ambiguity, and repeated descriptions caused by human languages. In this study, we did a simple language similarity checking to understand the database status. We also applied a logical representation system in our database to prove this concept.
"],"dc:identifier":["https://digitalcommons.kennesaw.edu/cs_etd/48"],"dc:subject":["Big Data","Database","Logical representation","Computer Sciences","Data Science"],"dc:title":["Using Big Data Analytics to Optimize Practical Large Databases"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Computer Science (MSCS)"]},"updated_at":"2026-07-24T02:43:51Z"}