Call for Papers
Without data, there is no machine learning (ML), so there is no doubt that big data and ML are inextricably linked. However, much research to date has tended to treat them as separate areas of development. As we are confronted with today’s difficult problems and the wealth of held data continues to grow, it is vital that new, innovative ways of examining, testing, and using big data to produce useful information are both researched/developed and integrated. Whether this be for the social good (health diagnostics, for example) or corporate gain (competitive advantage), given the exponentially increase in both the volume of data and the velocity by which it is generated, the need for the expansion of direct cooperation of mining big data with ML is long overdue. For this Special Issue, as the individual fields of advanced machine learning and advanced data mining are well established, the focus will be specifically on their intersection: the point―or points―at which one aids, needs, or enhances the other.
This new frontier is almost boundless, but will eventually become the norm. Automatically learning and improving from experience without being explicitly programmed gives great opportunities. The quality of the data being used, its speed of acquisition, and the effectiveness of processing are all of vital importance―if Microsoft’s AI chatbot Tay taught us anything at all, it is certainly this.
But can ‘big data’ ever be too much data? Is ML only suited to small datasets, allowing more focused training? And is there a real concern for data privacy where we try to combine big data with ML? (For example, does this issue come into sharp focus particularly where social media is concerned?) Many have been lulled into a false sense of security when using these systems, many of which offer a treasure trove of data. Or, to take an entirely different direction, is there space for big data and ML in the judiciary: could consistency of sentencing be applied, for example. We cannot list all the potential application areas here, but the scope for exciting research at the boundary of big data and ML should be clear. In addition, of course, this new frontier in artificial intelligence research offers as many ethical questions as it does possibilities: could we, should we, and (how) will we?
This Special Issue solicits empirical, experimental, methodological, and theoretical research reporting original and unpublished results on big data and machine learning analysis and mining on topics in all realms of research along with applications to real life situations.
Interests: cybersecurity; privacy; AI privacy
Interests: futurology; AI; big data; IoT; automation; technology ethics
Special Issues and Collections in MDPI journals
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access quarterly journal published by MDPI.
- big data
- machine learning
- future technologies