Open Educational Resources | Pao Yue-kong Library, The Hong Kong Polytechnic University

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Agile Software Development: It’s Implication for AI products and Developers

Software developments is advancing the technological world today. This changes have far more reaching implications in I.T industries such as Big data, Artificial intelligence and Agile Software development methodologies. Competition in the software development ecosystem has made developers to build software that are quick and reliable and often referred to as Agile development. Agile transformation is real and requires rethinking the business management, recruitment process and data strategy in a bid to stimulate disruptive solutions from within in-house development and deployment. AI product development would require rapid transformational changes within any organization. This can be accomplished by establishing specific operating models that permit development teams with the freedom of technology choice. This publication highlights some operating models that can be adopted to improve the success of AI products using Agile software development methodologies.

Subjects:: Computing, Data Science and Artificial Intelligence
Keywords:: Agile software development
Resource Type:: Others

Presentation

OntoNaviERP: Ontology-supported Navigation in ERP Software Documentation

This video was recorded at 7th International Semantic Web Conference (ISWC), Karlsruhe 2008. The documentation of Enterprise Research Planning (ERP) systems is usually (1) extremely large and (2) combines various views from the business and the technical implementation perspective. Also, a very specific vocabulary has evolved, in particular in the SAP domain (e.g. SAP Solution Maps or SAP software module names). This vocabulary is not clearly mapped to business management terminology and concepts. It is a well-known problem in practice that searching in SAP ERP documentation is difficult, because it requires in-depth knowledge of a large and proprietary terminology. We propose to use ontologies and automatic annotation of such large HTML software documentation in order to improve the usability and accessibility, namely of ERP help files. In order to achieve that, we have developed an ontology and prototype for SAP ERP 6.0. Our approach integrates concepts and lexical resources from (1) business management terminology, (2) SAP business terminology, (3) SAP system terminology, and (4) Wordnet synsets. We use standard GATE/KIM technology to annotate SAP help documentation with respective references to our ontology. Eventually, our approach consolidates the knowledge contained in the SAP help functionality at a conceptual level. This allows users to express their queries using a terminology they are familiar with, e.g. referring to general management terms. Despite a widely automated ontology construction process and a simplistic annotation strategy with minimal human intervention, we experienced convincing results. For an average query linked to an action and a topic, our technology returns more than 3 relevant resources, while a naïve term-based search returns on average only about 0.2 relevant resources.

Subjects:: Computing, Data Science and Artificial Intelligence and Management
Keywords:: Software documentation Enterprise resource planning Ontologies (Information retrieval)
Resource Type:: Presentation

Presentation

Linked Data at The New York Times: The First 161 Years

This video was recorded at 11th International Semantic Web Conference (ISWC), Boston 2012. The New York Times committment to Linked Data began over 160 years ago. Starting in 1851, The New York Times has always catalogued its archival articles using a controlled vocabulary of people, places, organizations and descriptors. In 2009 The New York Times started publishing this vocabulary as linked data using semantic web standards. In 2011 The Times announced the launch of several RESTful Semantic APIs. And in late 2012 and early 2013, The Times will migrate its entire process for vocabulary management to a system designed around the principles of Linked Data. In my remarks, I will survey the history of Semantic publishing at The New York Times, outline our semantic strategy, detail the business-case for linked data at The Times and provide an in-depth explanation of our new vocabulary management system.

Subjects:: Computing, Data Science and Artificial Intelligence and Management
Keywords:: New York times Linked data
Resource Type:: Presentation

Courseware

Big Data Strategies to Transform Your Business

While big data infiltrates all walks of life, most firms have not changed sufficiently to meet the challenges that come with it. In this course, you will learn how to develop a big data strategy, transform your business model and your organization. This course will enable professionals to take their organization and their own career to the next level, regardless of their background and position. Professionals will learn how to be in charge of big data instead of being subject to it. In particular, they will become familiar with tools to: - assess their current situation regarding potential big data-induced changes of a disruptive nature, - identify their options for successfully integrating big data in their strategy, business model and organization, or if not possible, how to exit quickly with as little loss as possible, and - strengthen their own position and that of their organization in our digitalized knowledge economy The course will build on the concepts of product life cycles, the business model canvas, organizational theory and digitalized management jobs (such as Chief Digital Officer or Chief Informatics Officer) to help you find the best way to deal with and benefit from big data induced changes. During the course, your most pressing questions will be answered in our feedback videos with the lecturer. In the assignments of the course, you will choose a sector and a stakeholder. For this, you will develop your own strategy and business model. This will help you identify the appropriate organizational structure and potential contributions and positions for yourself.

Subjects:: Computing, Data Science and Artificial Intelligence and Management
Keywords:: Business -- Data processing Big data
Resource Type:: Courseware

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