Simon Price, Ph.D.

- State-of-the-art tools from machine learning and artificial intelligence are making inroads to automate parts of the peer review process; however, many opportunities for further improvement remain.

- Profiling, matching and open-world expert finding are key tasks that can be addressed using feature-based representations commonly used in machine learning.

- Such streamlining tools also offer perspectives on how the peer review process might be improved: in particular, the idea of… Show more

- State-of-the-art tools from machine learning and artificial intelligence are making inroads to automate parts of the peer review process; however, many opportunities for further improvement remain.

- Profiling, matching and open-world expert finding are key tasks that can be addressed using feature-based representations commonly used in machine learning.

- Such streamlining tools also offer perspectives on how the peer review process might be improved: in particular, the idea of profiling naturally leads to a view of peer review being aimed at finding the best publication venue (if any) for a submitted paper.

- Creating a more global embedding for the peer review process which transcends individual conferences or conference series by means of persistent reviewer and author profiles is key, in our opinion, to a more robust and less arbitrary peer review process. Show less

Globally, over 500 universities now offer data science courses at undergraduate or postgraduate level and, in research-intensive universities, these courses are typically underpinned by academic research in statistics, machine learning and computer science departments and, increasingly, in multidisciplinary data science institutes. Much has been written about the academic challenges of data science from the perspective of its core academic disciplines and from its application domains, ranging… Show more

Globally, over 500 universities now offer data science courses at undergraduate or postgraduate level and, in research-intensive universities, these courses are typically underpinned by academic research in statistics, machine learning and computer science departments and, increasingly, in multidisciplinary data science institutes. Much has been written about the academic challenges of data science from the perspective of its core academic disciplines and from its application domains, ranging from sciences and engineering through to arts and humanities. However, relatively little has been written about the institutional information technology (IT) support challenges entailed by this rapid growth in data science. This paper sets out some of these IT challenges and examines competing support strategies, service design and financial models through the lens of academic IT support services. Show less

We introduce a data flow model that supports highly parallelisable design patterns, but which also has useful properties for analysing data serially over extended time periods without requiring traditional Big Data computing facilities. The model ranges over a class of higher-order relations which are sufficiently expressive to represent a wide variety of unstructured, semi-structured and structured data. Using JSONMatch, our web service implementation of the model, we show that the combination… Show more

We introduce a data flow model that supports highly parallelisable design patterns, but which also has useful properties for analysing data serially over extended time periods without requiring traditional Big Data computing facilities. The model ranges over a class of higher-order relations which are sufficiently expressive to represent a wide variety of unstructured, semi-structured and structured data. Using JSONMatch, our web service implementation of the model, we show that the combination of this model and higher-order representation provides a powerful and extensible framework that is particularly well suited to analysing Big Variety data in a web application context. Show less

We define a relational algebra on basic terms, strongly typed terms in a higher-order logic, that are well suited to the representation of heterogeneous data, irrespective of whether the data originated from relational, unstructured, semi-structured or structured sources. This higher-order generalisation of the relational model has potential applications in NoSQL databases and Big Variety, Big Data applications.

Coding standards and good practices are fundamental to a disciplined approach to software projects irrespective of programming languages being employed. Prolog programming can benefit from such an approach, perhaps more than programming in other languages. Despite this, no widely accepted standards and practices seem to have emerged till now. The present paper is a first step toward filling this void: It provides immediate guidelines for code layout, naming conventions, documentation, proper… Show more

Coding standards and good practices are fundamental to a disciplined approach to software projects irrespective of programming languages being employed. Prolog programming can benefit from such an approach, perhaps more than programming in other languages. Despite this, no widely accepted standards and practices seem to have emerged till now. The present paper is a first step toward filling this void: It provides immediate guidelines for code layout, naming conventions, documentation, proper use of Prolog features, program development, debugging, and testing. Presented with each guideline is its rationale and, where sensible options exist, illustrations of the relative pros and cons for each alternative. A coding standard should always be selected on a per-project basis, based on a host of issues pertinent to any given programming project; for this reason the paper goes beyond the mere provision of normative guidelines by discussing key factors and important criteria that should be taken into account when deciding on a full-fledged coding standard for the project. Show less

The SIGKDD'09 Research Track received 537 paper submissions, which were reviewed by a Program Committee of 199 members, and a Senior Program Committee of 22 members. We used techniques from artificial intelligence and data mining to streamline and support this complicated process at three crucial stages: bidding by PC members on papers, assigning papers to reviewers, and calibrating scores obtained from the reviews. In this paper we report on the approaches taken, evaluate how well they worked,… Show more

The SIGKDD'09 Research Track received 537 paper submissions, which were reviewed by a Program Committee of 199 members, and a Senior Program Committee of 22 members. We used techniques from artificial intelligence and data mining to streamline and support this complicated process at three crucial stages: bidding by PC members on papers, assigning papers to reviewers, and calibrating scores obtained from the reviews. In this paper we report on the approaches taken, evaluate how well they worked, and describe some further work done after the conference. Show less