The "
Symposia on Mathematical Techniques Applied to Data Analysis and Processing (SMATAD)" aim to create a collaborative environment for the exchange of ideas related to application of mathematics to data science. The event is divided into four symposia related to four different areas, namely:

  • Contradiction and lack of information in knowledge data bases,
  • Formal Concept Analysis,
  • Fuzzy techniques in Image Processing,
  • Forecasting and optimization for data-driven decision-making under uncertainty.

The idea of considering four different topics is based on the following two main ideas:
every two areas of Mathematics are somehow related and the most creative ideas come from ignorance. Let us be more specific with the description of each of those symposia.

Contradiction and lack of information in knowledge-data-bases

The new era of information (the Internet of things, the worldwide web, etc) is leading to a world overwhelmed by data. One of the main features of such information is that it is usually incomplete or it brings about contradictions. This symposium covers all those topics which deal with the management of incomplete or contradictory information in databases. Some topics
(non-exhaustive) are:

  • Analysis of inconsistencies in logic theories,
  • Measures of contradiction,
  • Repairing techniques of incomplete information,
  • Non-classical logic theories,
  • Big Data,
  • Belief systems,
  • Non-monotonic reasoning.

Formal Concept Analysis

Formal Concept Analysis emerged in the 1980′s from attempts to restructure lattice theory in order to promote better communication between lattice theorists and potential users of lattice theory. Since its early years, Formal Concept Analysis has developed into a research field in its own right with a thriving theoretical community and a rapidly expanding range of applications in information and knowledge processing including visualization, data analysis (mining) and knowledge management. The (non-exhaustive) list of topics of interest include:

  • FCA theory
  • Lattice Theory
  • Lattice Drawing
  • Philosophical Foundations
  • FCA and Logic
  • Conceptual Knowledge
  • Applications of hierarchical classification and data organization
  • Machine learning
  • Concept Graphs
  • Data Analysis
  • FCA and Data Mining
  • Association Rules and Pattern Mining
  • Analysis of Social Networks
  • Algorithms and Complexity Theory
  • FCA and Software Engineering
  • Biclustering and n-relational clustering

Fuzzy techniques in Image Processing

are formally sets of pixels (i.e., a discrete subset of the real plane) with a degree of intensity. On the other hand, fuzzy sets theory is developed with the goal of handling degrees of membership of elements in a certain subset (called universe). Therefore, it is natural to expect an interaction among both theories. This symposium is oriented to the presentation and discussion of such interaction. The list of topics of interest (not all) are:

  • Pattern recognition,
  • Machine vision,
  • Filtering,
  • Video Processing,
  • Medical Image Analysis,
  • Compression, storage and retrieval techniques,
  • Restoration.

Forecasting and optimization for data-driven decision-making under uncertainty

The so-called
Information Age has brought the exponential growth and availability of data. These have allowed us not only to understand our environment, our bodies and our social interactions in ways we could have never imagined before, but also to shape the state of things by developing and manufacturing new systems, products and mechanisms. Humans use data to learn, build, create... and to make decisions. In rational decision-making, data is primarily used to produce mathematical models for the processes that determine how good or bad our decisions are. We then use these models to forecast the future behaviour of these processes, and finally, optimize our decisions based on those forecasts. This symposium will bring together new mathematical models to optimize decision-making under an uncertain environment and will explore the roles of advanced forecasting and optimization in leveraging big data to improve the operation and planning of intelligent systems. The symposium is, therefore, expected to cover a variety of topics such as:
  • Techniques for optimization under uncertainty: stochastic, robust and chance-constrained programming.
  • Hierarchical optimization and complementarity modeling. MPECs and EPECs.
  • Time series analysis, dynamic factor models.
  • Machine/statistical learning.
  • From predictive to prescriptive analytics: new techniques for bridging the gap between forecasting and optimization.
  • Applications to smart grids and intelligent energy systems.