Cognitive Computing

Cognitive and Contextual Analytics for IT Services

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Journal of R&D: Cognitive and Contextual Analytics New Issue of the IBM Journal of Research and Development

Our ability to monitor and collect data about IT (information technology) services, ranging from cloud and hosted-IT delivery models to maintenance services, has grown to unprecedented levels over the past few years.

Our latest issue of the IBM Journal of Research and Development emphasizes new solutions, models, capabilities and other technologies that represent important advancements towards cognitive and contextual analytics for IT services. The journal is a peer-reviewed technical journal, published quarterly, which features the work of authors in the science, technology, and engineering of information systems.

In this issue, our guest editors – Alan Bivens, executive assistant to the General Manager, IBM Blockchain, and Hari Ramasamy, senior manager of Watson Platform for Health – note that data growth has enticed many practitioners and researchers to build innovative analytical capabilities to gain insights from the data, as well as cognitive capabilities to help IT professionals more effectively make use of this growth in data.

Additionally, client requirements for agility, and technologies like cloud computing, have served to bring the many facets of IT services closer to one another (e.g., development and operations, infrastructure services and maintenance services, etc.). Analytics that span these previously separated areas of IT services have the potential to foster dramatic new efficiencies in end-to-end IT delivery. For example, some cloud-native analytic capabilities combine code/library repository data (development data) with runtime log data (operational data) to determine code changes that lead to workload failures.

Topics in this issue include end-to-end distributed solutions, problem extraction and search, predictive maintenance and upgrades, and much more. Note that the use of the term cognitive is often meant to imply some aspect of understanding, reasoning, and learning. The papers in this issue may include one or more of these aspects, touching upon such topics as automatic problem extraction and analysis from unstructured text; continuous learning from user interactions and feedback; machine learning and learning-based drift analytics tools; and systems that try to “understand” how humans perform problem determination and resolution, while encoding and automating this process, for example.

Interested readers may visit the IBM Journal website for a sampling of recent issues, all available in the IEEE Xplore Digital Library.


Clifford A. Pickover
IBM Journal of Research and Development


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