IEEE BigData coming soon
This year IEEE BigData 2020 taking place on-line from Atlanta (USA). Virtually, but organized with the same commitment, the conference will take place in mid-December.
The IEEE International Conference on Big Data 2020 provides a leading forum for disseminating the latest research in Big Data. IEEE Big Data brings together leading researchers and developers from academia, research, and the industry from all over the world to facilitate innovation, knowledge transfer, and the technical progress in addressing the 5 V’s (Velocity, Volume, Variety, Value and Veracity) of Big Data.
The purpose of the conference is to identify the deep technical and scientific nature of big data problems and share the future direction on the development of next-generation solutions for data-driven decision making. The conference will attract high-quality theory and applied research findings in big data science and foundations, big data infrastructure, big data management, big data search & mining, privacy/security, and big data applications. The attendees will get the opportunity to learn about the recent research results, and be able to network with some of the leading luminaries, researchers, and practitioners in this area.
Special Session on IEEE BigData
The QED Software team significantly contributed to organization of this event. Dominik Ślęzak (Institute of Informatics, University of Warsaw / QED Software, Founder&CEO) is the co-organizer of a Special Session „Information Granulation in Data Science and Scalable Computing”. Special Session will continue to address the theory and practice of computation of information granules. It will provide researchers from universities, laboratories , and industry with the means to present state-of-the-art research results and methodologies for information granules. The session will also make it possible for scientists and developers to highlight their new research directions and new interactions with novel computing models for information granulation.
The session will focus particularly on currently important research tracks such as social network computing, cloud computing, cyber-security, data mining, machine learning, knowledge management, AI-based systems, intelligent systems , and soft computing (neural networks, fuzzy systems, evolutionary computation, rough sets, self-organizing systems), e-Intelligence (Web Intelligence, Semantic Web, Web Informatics), business informatics, bioinformatics, and medical informatics.
Apart from Dominik Ślęzak, the organizing committee includes: Shusaku Tsumoto (Department of Medical Informatics, Faculty of Medicine, Shimane University, Japan), Tzung-Pei Hong (Department of Computer Science and Information Engineering, National University of Kaohsiung), S. L. Wang (Department of Information Management, National University of Kaohsiung), Weiping Ding (School of Information Science and Technology, Nantong University).
In addition to the Special Session, the QED Software team will also be at the event by presenting two studies:
“Reinventing Infobright’s Concept of Rough Calculations on Granulated Tables for the Purpose of Accelerating Modern Data Processing Frameworks”
- Mateusz Wnuk
- Sebastian Stawicki
- Dominik Ślęzak
“Predicting Escalations in Customer Support: Analysis of Data Mining Challenge Results”
- Dominik Ślęzak
- Andrzej Janusz
Both lectures will be held on December 10.
The first study is related to our project titled “Small Big Data”, which aims to create a scalable Machine Learning technology. But BigData – it’s not only a buzzword. What if the data will be generated faster than disk resources will increase? With big, bigger and yet bigger data sets the question arises do we need ALL the data? People don’t seem to remember everything that happens to them, and yet they work quite effectively. Most probably, the ability to selectively forget is the key to intelligence. At QED Software we develop Artificial Intelligence solutions for pioneers, and so we care deeply about effective utilization of the resources needed for computing. When the amount of generated data is too big to consume (store, curate, index, etc.) a need for methods that deal with that sort of problems arises naturally. Can we live with somewhat approximate solutions at the expense of actually being able to compute them? Accordingly, the goal of this project is development of novel methods of summarizing very large data sets to improve the effectiveness of machine learning algorithms for large-scale problems. We aim at addressing some of the most pressing questions: How to create data summaries that contain the most relevant information? How to create these summaries for different types of data: tables, images, sounds, texts, relations, movies, space-time …? Can models be trained directly on the summaries? Which methods are adaptable to this and which methods can be used without modification?
“Predicting Escalations in Customer Support: Analysis of Data Mining Challenge Results” is a work that resulted from a competition organized by QED Software on the KnowlegdePit platform in cooperation with Information Builders, Inc. (ibi).
Technical Support Representatives of Information Builders, Inc. (ibi) strive to provide the highest quality level of support to their customers. At times, we may encounter situations where our support process and the needs of our ccustomers’ustomers conflict. When this occurs, undoubtedly an escalation will arise. Every escalation is very disruptive to the support process, it changes the day to day activities of Technical Support Representatives , and more importantly, we have an upset customer. The ability to predict when an escalation may arise will allow us to react and do what’s possible to prevent an escalation, diffuse a potential problem, thus maintaining customer satisfaction. We should be able to predict “when” an escalation occurs, it is also equally important to predict why an escalation is going to arise – is it due to a production outage, duration, technical proficiency, project deadlines or other issues. Depending upon the type of escalation we will be able to build differing support processes that can be best suited to prevent an escalation.
This competition – aiming at building models that predict whether particular customer success cases are going to escalate in the future based on information about their up-to-now history – is an important step for ibi to provide their customers with better services relying on modern machine learning solutions.
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