A search tool to enhance the selection and utilisation of reusable software modules within the object-oriented paradigm

The affinity for reuse within the object-oriented paradigm may enable high levels of productivity; however, gains will become realisable only if a systems developer has access to tools which aid in the selection of classes. A method for object-oriented analysis and design is detailed and its process is assisted by an object-oriented search tool based on reference and corporate library technology. The search tool contributes to the determination of suitable existing inheritable classes and an explanation of its construction and use is included. A practical demonstration of the method, using the search tool, is elaborated. The thesis demonstrates that text retrieval techniques used in modern libraries may be successfully applied to determine suitable classes for the object-oriented paradigm

Designing a reusable class cluster: a hypertext cluster

This thesis is a contribution to the study of object-oriented software engineering, focusing upon the reuse-approach to build reliable and extendable software. A hypertext system has been developed to study the reuse. The ET++ and MacApp framework class libraries provide standard components that can be re-used to develop application-specific programs. A plug-compatible design is presented in this work; this design approach facilitates building applications that work with frameworks based on similar principles running on different platforms. Hypertext is chosen as an example domain for designing class clusters, while ET++ and MacApp are selected as frameworks. A survey of reuse and hypertext is done at various levels. The hypertext specific classes are designed, keeping the commonalities in mind to provide adequate generalization. These classes are implemented on the ET++ and MacApp frameworks. There are variations in the two implementations, but overall design of the classes remains the same. The design is validated by testing it against the changing requirements of the system and adding new functionality to the system. The effectiveness of the plug-compatible approach is explored

Three Essays on Big Data Consumer Analytics in E-Commerce

Consumers are increasingly spending more time and money online. Business to consumer e-commerce is growing on average of 20 percent each year and has reached 1.5 trillion dollars globally in 2014. Given the scale and growth of consumer online purchase and usage data, firms\u27 ability to understand and utilize this data is becoming an essential competitive strategy. But, large-scale data analytics in e-commerce is still at its nascent stage and there is much to be learned in all aspects of e-commerce. Successful analytics on big data often require a combination of both data mining and econometrics: data mining to reduce or structure (from unstructured data such as text, photo, and video) large-scale data and econometric analyses to truly understand and assign causality to interesting patterns. In my dissertation, I study how firms can better utilize big data analytics and specific applications of machine learning techniques for improved e-commerce using theory-driven econometrical and experimental studies. I show that e-commerce managers can now formulate data-driven strategies for many aspect of business including cross-selling via recommenders on sales sites to increasing brand awareness and leads via social media content-engineered-marketing. These results are readily actionable with far-reaching economical consequences