Interpretable Categorization of Heterogeneous Time Series Data

Understanding heterogeneous multivariate time series data is important in many applications ranging from smart homes to aviation. Learning models of heterogeneous multivariate time series that are also human-interpretable is challenging and not adequately addressed by the existing literature. We propose grammar-based decision trees (GBDTs) and an algorithm for learning them. GBDTs extend decision trees with a grammar framework. Logical expressions derived from a context-free grammar are used for branching in place of simple thresholds on attributes. The added expressivity enables support for a wide range of data types while retaining the interpretability of decision trees. In particular, when a grammar based on temporal logic is used, we show that GBDTs can be used for the interpretable classi cation of high-dimensional and heterogeneous time series data. Furthermore, we show how GBDTs can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply GBDTs to analyze the classic Australian Sign Language dataset as well as data on near mid-air collisions (NMACs). The NMAC data comes from aircraft simulations used in the development of the next-generation Airborne Collision Avoidance System (ACAS X).Comment: 9 pages, 5 figures, 2 tables, SIAM International Conference on Data Mining (SDM) 201

Statistical Inferences for Polarity Identification in Natural Language

Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes a novel method of studying the reception of granular expressions in natural language. The approach utilizes LASSO regularization as a statistical tool to extract decisive words from textual content and draw statistical inferences based on the correspondence between the occurrences of words and an exogenous response variable. Accordingly, the method immediately suggests significant implications for social sciences and Information Systems research: everyone can now identify text segments and word choices that are statistically relevant to authors or readers and, based on this knowledge, test hypotheses from behavioral research. We demonstrate the contribution of our method by examining how authors communicate subjective information through narrative materials. This allows us to answer the question of which words to choose when communicating negative information. On the other hand, we show that investors trade not only upon facts in financial disclosures but are distracted by filler words and non-informative language. Practitioners - for example those in the fields of investor communications or marketing - can exploit our insights to enhance their writings based on the true perception of word choice

DESQ: Frequent Sequence Mining with Subsequence Constraints

Frequent sequence mining methods often make use of constraints to control which subsequences should be mined. A variety of such subsequence constraints has been studied in the literature, including length, gap, span, regular-expression, and hierarchy constraints. In this paper, we show that many subsequence constraints---including and beyond those considered in the literature---can be unified in a single framework. A unified treatment allows researchers to study jointly many types of subsequence constraints (instead of each one individually) and helps to improve usability of pattern mining systems for practitioners. In more detail, we propose a set of simple and intuitive "pattern expressions" to describe subsequence constraints and explore algorithms for efficiently mining frequent subsequences under such general constraints. Our algorithms translate pattern expressions to compressed finite state transducers, which we use as computational model, and simulate these transducers in a way suitable for frequent sequence mining. Our experimental study on real-world datasets indicates that our algorithms---although more general---are competitive to existing state-of-the-art algorithms.Comment: Long version of the paper accepted at the IEEE ICDM 2016 conferenc

The Development of a Temporal Information Dictionary for Social Media Analytics

Dictionaries have been used to analyze text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist