Did you mean: Search also for broader subjects Search also for narrower subjects Search also for related subjects Beebe, Susan J. ISLAM-SURAH KAUSAR SAY BEEMARION KA ILAJ ISLAM-SURAH MUZZAMMIL SAY BEEMARION KA ILAJ ISLAM-SURAH TAUBAH SAY BEEMARION KA ILAJ Having been a subaltern MS-MECH-84 MSTHESIS ABSTRACT. Reconfigurable Manufacturing Systems (RMS) effectively respond to fluctuating market needs and customer demands for finished product. Diagnosability is a supporting characteristic of RMS that has a say in the quality of finished product. Cost and time taken for manufacturing are also considerably affected if proper diagnosability measures are not taken. Previous studies on Diagnosability of RMS have been studied from Axiomatic System Theory as such Design For Diagnosability (DFD). Nevertheless Diagnosability remains to be the least studied characteristic of RMS. With the availability of digitized data, Machine Learning approaches to advance manufacturing have proven to be considerably effective. A research gap existed for the application of Machine Learning techniques in improving the Diagnosability of RMS. A framework of Machine Learning has been proposed to address this gap. The working of the framework has been illustrated by two demonstrations from the available datasets, one in identifying proper signals in semi-conductor manufacturing to predict excursions, and the second in predicting machine failures due to a variety of factors. The framework is rendered in a concurrent-engineering fashion. The framework is tested against two available manufacturing datasets. Increase in Diagnosability will decrease the cost and time taken to production. Key Words: Reconfigurable Manufacturing Systems, Machine Learning, Artificial Intelligence, Preventive Maintenance, Intelligent Manufacturing MS-CSE-15 MSTHESIS ABSTRACT. Database management Systems (DBMS) are one the most critical component of a software application. Searching data from DBMS is an enormous part in software performance. Text search engines are also used for searching, but these engines lack sophisticated DBMS features. Relational database management systems (RDBMS) are not quite compatible with modern objectoriented languages. To overcome the complexity of data and object-oriented programming, modern development practices adopted Object Relation Mapping frameworks (ORM). ORM bears a layer of abstraction between object models and database. This layer automatically bridges objects in OOP languages to database records, which results in significantly reducing custom mapping code complexity. ORM has its advantages but on the other side it comes with be some challenges too. In process of mapping objects and data, ORM keeps the relations between objects intact and that results in retrieval of multiple objects from multiple tables. When the data is big and have a hieratical structure, data retrieval or search becomes more complex. Database performance for the retrieval of data are optimized by adding indexing to each table. Indexing makes search significantly fast but also makes other processes slow because tables are required to be re-index every time a record is changed. Hence an optimized solution is required to resolve this problem in ORM search process. To overcome this problem, this research proposes a java-based framework that can interact between ORM and search engine. It consumes search engine web APIs to provide a layer that can convert and search objects to/from XML. It makes search process faster and support ORM with its object-oriented methodology. Moreover, this framework not only reduces performance load on databases but also makes search queries simpler when implemented in development process. The results have been validated by two case studies, which were carried out by implementing each approach. 1000 similar search queries were processed on each framework and results shows 30 to 40 % improvement in query time. Keywords: DBMS Search, Indexing, Text Search Engines, Solr inedexes, Object oriented programming (OOP), Object Relation Mapping (ORM), Search optimization, Information Retrieval, database indexing