Workshops at ICDL 2016

ICDL 2016

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Workshops

ICDL 2016 is pleased to announce workshops which will be conducted in parallel to ICDL 2016 sessions for discussion on specialized issues relevant to knowledge management, big data and social media analytics across the globe and their impact on knowledge society. A learned panel of experts will deliver views and share experiences on the issues.

Innovation and Knowledge Management

14 December 2016 09:30-5:30 pm Juniper

Big Data and Analytics

15 December 2016 09:30-5:30 pm Juniper

Social Media and Analytics

16 December 2016 09:30-5:30 pm Juniper

Workshop on - Innovation and Knowledge Management

Theoretical knowledge is one thing
Knowing how to turn knowledge into success is the key difference between average and excellent decision-makers.

Knowledge Management (KM), which has evolved over the past several years, stresses the importance of using KM to enhance knowledge production in organizations, and not just knowledge sharing or integration. Thus, KM can have an impact on the quality of problem-solving in business and can also lower risk and improve innovation along the way. It is a tool for creating quality-control systems for knowledge and innovation in organizations.

This workshop will show you how KM can help:

Improve your skill set and competencies by readily applying innovation knowledge and skills in innovation projects and initiatives within your organization
Increase your value to your organization by utilizing innovation tools, methodology, and best practices to deliver successful and high impact results
Network and exchange ideas, experiences, and insights with other innovation professionals
Enhance your career prospects and professional growth through our in-person and online training opportunities
Learn about the Knowledge Life Cycle as a target of KM interventions
Understand a case study of KM success, illustrating a key intervention pattern called the Open Enterprise Pattern that provides the basis for a comprehensive KM strategy
Learn an incremental, bottom-up strategy and a key method for reaching the Open Enterprise, a normative model for enhanced problem solving, sustainable innovation, risk reduction and management, and corporate social responsibility.

Agenda

Workshop on - Big Data and Analytics

Big Data has become ubiquitous in modern society. It challenges state-of-the-art data acquisition, computation, and analysis methods. Much focus has been placed on the application of Big Data methods and less focus is placed on the theoretical underpinning of the field.

The new availability of data—administrative records, mobile devices, sensors, and many private sources – as well as new processing and analytical techniques, has the potential to transform the practice of science. In the social science context, the new data can potentially offer information for policymakers that is much more current, granular, and richer in environmental information than data produced by statistical agencies from surveys. Yet with the unfolding of new research opportunities, there are challenges associated with making use of the new data that are no longer generated and disseminated by statistical agencies, but can be harvested from many individual, public, and some private actions.

Nonetheless, important scholarly work has been done that uses big data in a way that is valuable to policymakers—in areas as varied as finance, labour, education, science, innovation, transportation, and development.

The workshop topics include, but are not limited to the following:

1. Big Data comprises bits on one side and processing on the other. As we enter the 5 Vs we are confronted with:

Volume: can present architectures scale to crunch the volume of data involved.
Velocity: the rapid and asynchronous change in data over several sources makes consistency a major issue and replication basically impossible.
Variety: differences are deeper than the expression of independent values based on different metrics. They relate to differences in the capturing of those values, in their stability, and in the credibility of the sources.
Value: who is benefiting from the metadata, how are data sources rewarded, and who is accountable?
Virtualization: this might be happening in the Internet of Things (IoT) where you don’t actually have Volume, nor Velocity, nor Variety in principle since in IoT a lot is happening locally. However the challenge for the future is to consider all of data spread around and look at them as a "virtual Big Data".
We would like to dedicate a deeper investigation of the Vs implication and see a small group of discussant bringing different perspectives to the floor, to extend the discussion to the audience with a resulting list of actions that this initiative will need to take.

2. How can we better understand Big Data?

What are the technologies for generating metadata (data analytics) and what are the technologies for rendering (visualization)?
How can we trust the outcome—Psychology of Perception?
How does the value shift from data to metadata, who is benefiting?This is OK

3. It is about Big or it is about Meta?

If we look at the set of sets, i.e. correlation, then in both cases we have meta-data generation and can we relate to Big Data issues?
How can we generate meta-information from data?
What are the changes needed to better exploit the data ecosystem?
What are the hurdles to solve?
How about addressing emerging information, semantics, and behaviours?

Agenda

Workshop on - Social Media and Analytics

The explosion of Social Media in the form of user-generated content on blogs, microblogs (Twitter), discussion forums, product reviews and multimedia sharing sites presents many new opportunities and challenges to both producers and consumers of information. For producers, this user-generated content provides a rich source of implicit consumer feedback. Tracking the pulse of the ever-expanding social media outlets, enables companies to discern what consumers are saying about their products, which provides useful insight on how to improve and market products better. For consumers, the plethora of information and opinions, from diverse sources, helps them tap into the wisdom of crowds, to aid in making more informed decisions. Though there is a vast quantity of information available, the consequent challenge is to be able to analyse the large volumes of user-generated content and the implicit (or explicit) links between users, in order to glean meaningful insights therein.

The workshop will raise address interesting questions related to blogs, Twitter, and other social media platforms:

Given the massive amount of user-generated content on the Web, how can we identify the subset of content (blogs, forums, etc.) that are discussing not only a specific entity, but higher level concepts that are in some way relevant to the product/brand/entity of interest?
Having identified this subset of relevant content, how do we identify the most authoritative or influential authors in this space?
How do we detect and characterize specific sentiment expressed about an entity (e.g. product) mentioned in a blog or forum?
How do we tease apart emerging topics of discussion (it is correct-no problem in it)from the constant chatter in the blogosphere and other social media?
How do we overcome the information overload and provide a rich and coherent user experience?
How to deal with unreliable and often conflicting information? What notions of trust are appropriate?

The goal of this workshop is to formalize and address meaningful metrics and predictive tasks, as well as actionable explanatory analysis, corresponding to the above objectives. This workshop will bring together researchers in all related disciplines, from academia, industry, and the government, and will create a forum for discussing recent advances in analysis of Social Media. In doing so we aim to better understand the practical requirements of this domain and the limitations of our current methods, and to inspire research on new algorithms and techniques for Social Media Analytics.

Agenda

LIST OF PARTICIPANTS Final Report