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Unprecedented volumes of location-based information have been produced as a result of the widespread adoption of social network applications and GPS-enabled devices and sensors. Publication of such location data can provide valuable resources for researchers and government agencies in applications ranging from near real-time population-wide health monitoring to planning for future cities. However,...
Large-scale time series data are prevalent across diverse application domains including system management, biomedical informatics, social networks, finance, etc. Temporal dependency discovery performs an essential part to identify the hidden interactions among the observed time series and helps to gain more insight into the behavior of the applications. However, the time-varying sparsity of the interactions...
Considering the fact that fully immunizing critical infrastructure such as water supply or power grid systems against physical and cyberattacks is not feasible, it is crucial for every public or private sector to invigorate the detective, predictive, and preventive mechanisms to minimize the risk of disruptions, resource loss or damage. This paper proposes a methodical approach to situation analysis...
Tables are significant document components. Table extraction and classification are critical for us to explore, retrieve and mine knowledge encoded in tables. This paper presents a learning based approach for classifying tables based on their content and structural information, with focus on financial document tables. To the best of our knowledge, this is the first study on classifying tables in financial...
Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives,...
We propose a new analytical method to classify web user behavior based on such latent states of users as intention, interest, or motivation. First, we put the clickstream data of many users into a Hidden Markov Model in which the number of hidden states is large enough to build a state transition network. Since the variable hidden states represent different latent states of users, the movement on...
Big data mining and unsupervised pattern recognition from large corpus of text-based documents has been an active research topic over the past decade. This paper presents a novel sequence of network-based models for identifying high-dimensional clustering patterns between topics for quantitative and predictive modeling of trends in Management Science (an INFORMS Journal) papers over the past 54 years...
Morphological analysis is an essential step for processing the Korean language, due to highly agglutinative properties of the language. In this paper, we propose a novel approach for constructing a Korean morphological analyzer that can capture linguistic properties using graphemes as basic processing units. Since our model does not utilize prior linguistic knowledge, the model can be applied to other...
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