Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

A survey of temporal knowledge discovery paradigms and methods

With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining

Putting the Semantics into Semantic Versioning

The long-standing aspiration for software reuse has made astonishing strides in the past few years. Many modern software development ecosystems now come with rich sets of publicly-available components contributed by the community. Downstream developers can leverage these upstream components, boosting their productivity. However, components evolve at their own pace. This imposes obligations on and yields benefits for downstream developers, especially since changes can be breaking, requiring additional downstream work to adapt to. Upgrading too late leaves downstream vulnerable to security issues and missing out on useful improvements; upgrading too early results in excess work. Semantic versioning has been proposed as an elegant mechanism to communicate levels of compatibility, enabling downstream developers to automate dependency upgrades. While it is questionable whether a version number can adequately characterize version compatibility in general, we argue that developers would greatly benefit from tools such as semantic version calculators to help them upgrade safely. The time is now for the research community to develop such tools: large component ecosystems exist and are accessible, component interactions have become observable through automated builds, and recent advances in program analysis make the development of relevant tools feasible. In particular, contracts (both traditional and lightweight) are a promising input to semantic versioning calculators, which can suggest whether an upgrade is likely to be safe.Comment: to be published as Onward! Essays 202

What May Visualization Processes Optimize?

In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.Comment: 10 page

Data Mining Techniques for Complex User-Generated Data

Nowadays, the amount of collected information is continuously growing in a variety of different domains. Data mining techniques are powerful instruments to effectively analyze these large data collections and extract hidden and useful knowledge. Vast amount of User-Generated Data (UGD) is being created every day, such as user behavior, user-generated content, user exploitation of available services and user mobility in different domains. Some common critical issues arise for the UGD analysis process such as the large dataset cardinality and dimensionality, the variable data distribution and inherent sparseness, and the heterogeneous data to model the different facets of the targeted domain. Consequently, the extraction of useful knowledge from such data collections is a challenging task, and proper data mining solutions should be devised for the problem under analysis. In this thesis work, we focus on the design and development of innovative solutions to support data mining activities over User-Generated Data characterised by different critical issues, via the integration of different data mining techniques in a unified frame- work. Real datasets coming from three example domains characterized by the above critical issues are considered as reference cases, i.e., health care, social network, and ur- ban environment domains. Experimental results show the effectiveness of the proposed approaches to discover useful knowledge from different domains

TALKING INSTITUTIONS IN THE SHARING ECONOMY: A CONTENT ANALYSIS OF ACTOR QUOTES IN THE PRINT MEDIA AND A TAXONOMY OF DISCURSIVE STRATEGIES

We study how actors engage in institutional work to manage legitimacy by influencing media discourse in the face of discontinuous innovation. We content-analyze actor quotes reproduced in newspaper articles about the ‘sharing economy’ in the taxi and lodging industries to survey this aspect of media discourse and offer a taxonomy of the discursive strategies used in the public debate on institutional change. We find that actor quotes are dominantly from offensive actors striving for institutional change, mostly due to a relatively low share of voice of incumbent firms as defensive actors aiming at institutional maintenance. Whereas offensive actors aimed for legitimacy in their discursive strategies by balancing attacks on existing institutions with assertions of new institutions, defensive actors aimed for legitimacy more by attacking new institutions than by reinforcing existing ones. Our findings suggest that, contrary to prior beliefs, preventing the emergence of new institutions plays a crucial role for defensive institutional work