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2 Hours

Brief Description

Cryptocurrencies, such as Bitcoin, have pioneered the blockchain technology, but it is important to separate these digital currencies from the underlying technology that has made these currencies possible. Proponents contend that blockchains have the promise to touch, if not disrupt, every major industry and will even alter the way that individuals, enterprises, societies and machines interact. A technology that can increase efficiency, reduce costs and promote transparency can have significant implications for a range of industry and social sectors. The potential to transform systems and leapfrog existing infrastructure can enable solutions that were previously not thought possible. Blockchain technology, with its de-centralized and consensus- driven network, peer-to-peer operation and cryptographic security, presents a new paradigm for how information can be collected and communicated. A system in which trust in a third party is no longer required opens the doors to a myriad of use cases. But the question remains: are we at the pinnacle of a history-altering technology that will drive massive change, or is the blockchain just hype without substance? An understanding of the structure and complexities of the blockchain is essential before one can argue the merits, or otherwise, of the technologies’ ability to influence our lives in the near future.



Lecturer

Dr. Mustafa Ally

School of Management & Enterprise, University of Southern Queensland
SPRINGFIELD CAMPUS
University of Southern Queensland
37 Sinnathamby Boulevard
Springfield Central
QLD 4300
allym@usq.edu.au



Short biography

Dr. Mustafa Ally is a Senior Lecturer in the School of Management and Enterprise in the Information Systems discipline. He designed and teaches a post-graduate course in crypto- currencies and has been involved in this field of research since 2013. He manages and administers a Special Interest group on Bitcoins, and has delivered a number of workshops on crypto-currencies. He currently supervisors PhD students investigating Blockchain use cases. Dr. Ally’s PhD was on the subject of online payment systems.

Half day tutorial – 4 hours

Brief Description

Several scholarly studies have analyzed how textual content of News, Analyst Reports, Consumer- generated content on social-media etc. impact financial data like stock indices or revenue. With humongous amount of such content being generated continuously on the web, developing predictive models that can effectively analyze this data to predict the behavior of financial data in real time is a challenging task. Joint modeling of textual and financial data for predicting financial outcomes is an interesting research area which is growing rapidly due to its wide-spread applicability and increasing user interest in this.



In this tutorial we shall be presenting a comprehensive overview of the area which will include the following:

  1. Problem formulation – This section will cover the different ways in which the problem has been formulated along with the different types of unstructured data sources considered and their significances. The sources are diverse in nature consisting of News, Reports, consumer content generated on Social Media, surveys, feedbacks etc.

  2. Information extraction - This section will cover the wide array of methods and techniques based on traditional machine Learning and recent Deep Learning (LSTM, Bi-LSTM, Tree LSTM etc.) that are applied to analyze the unstructured text content to extract information components like entities, relations, topics, intent, events and also derived concepts like relations, sentiments and behavioral information from the source data.

  3. Joint modeling – This section will cover the predictive modeling techniques that are applied for predicting the financial outcomes taking into account the information derived from the unstructured content and fusing it with past financial data for better predictions.

  4. An Architectural Framework - In this section we will be discussing the platform components that are needed to build a real-time event-based predictive system. It will cover the entire Information acquisition, processing, storage and retrieval pipeline using Open source Natural Language Processing (NLP) libraries (Spacy, NLTK) and Machine Learning based predictive models implemented over real-time distributed architecture like Spark.


Lecturers

Dr. Lipika Dey

Principal Scientist and Ishan Verma, Researcher
TCS Innovation Labs,
Tata Consultancy Services, India
Delhi
lipika.dey@tcs.com and lipikadey@gmail.com



Short biography

Dr. Lipika Dey is a Senior Consultant and Principal Scientist at Tata Consultancy Services, India. She heads the Research and Innovation program for TCS Analytics and Insights business. Her focus is on seamless integration of social intelligence and business intelligence using multi-structured data analytics. Her research interests are in the areas of Natural Language Processing, Text and Data Mining, Machine Learning and Semantic Search. She is in the Program Committee of several Data Mining and NLP conferences like KDD, WWW etc. She, along with her colleague Hardik Meisheri had delivered a tutorial at WI 2017, which was very well-received. The tutorial material was later published as an invited paper in IEEE Intelligent Informatics Bulletin.



Lipika did her Integrated M.Sc. in Mathematics, M.Tech in Computer Science and Data Processing and a Ph.D. in Computer Science and Engineering - all from IIT Kharagpur, India. Prior to joining TCS in 2007, she was a faculty at the Department of Mathematics at IIT Delhi, India from 1995 to 2007.



Lipika has been publishing consistently in Tier 1 conferences and has also been invited to speak at several Business Conferences like Sentiment Analysis Symposium, San Francisco and New York, Text Analytics Summit at Boston and Language Technology Accelerate, Brussels.

Lipika and Ishan jointly own several patents on Enterprise Content Analysis.



Short biography

Ishan Verma


Ishan Verma is a Researcher at Tata Consultancy Services, India with over 7 years of experience in Industrial R&D. He is a part of Research and Innovation program for TCS Analytics and Insights business unit at TCS Innovation Labs, Delhi. His research focus is on enterprise context driven multi- structured data analytics. Ishan's research interests are in the areas of text analytics, information extraction and retrieval, web-intelligence, semantic search, machine learning and integrated analytics of structured & unstructured data. Ishan is currently pursuing his M.Tech in Software Systems with specialization in Data Analytics from BITS, Pilani. Ishan did his B.Tech. in Computer Science and Engineering from IET Lucknow.