Periodic Pattern Mining a Algorithms and Applications

Owing to a large number of applications periodic pattern mining has been extensively studied for over a decade Periodic pattern is a pattern that repeats itself with a specific period in a give sequence Periodic patterns can be mined from datasets like biological sequences continuous and discrete time series data spatiotemporal data and social networks Periodic patterns are classified based on different criteria Periodic patterns are categorized as frequent periodic patterns and statistically significant patterns based on the frequency of occurrence Frequent periodic patterns are in turn classified as perfect and imperfect periodic patterns full and partial periodic patterns synchronous and asynchronous periodic patterns dense periodic patterns approximate periodic patterns This paper presents a survey of the state of art research on periodic pattern mining algorithms and their application areas A discussion of merits and demerits of these algorithms was given The paper also presents a brief overview of algorithms that can be applied for specific types of datasets like spatiotemporal data and social network

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The Proceeding of the annual joint workshop of the Fraunhofer IOSB and the Vision and Fusion Laboratory (IES) 2018 of the KIT contain technical reports of the PhD-stundents on the status of their research. The discussed topics ranging from computer vision and optical metrology to network security and machine learning. This volume provides a comprehensive and up-to-date overview of the research program of the IES Laboratory and the Fraunhofer IOSB

Applied Remote Sensing Program (ARSP)

Descriptions of projects engaged by the Applied Remote Sensors Program in the state of Arizona are contained in an annual report for the fiscal year 1976-1977. Remote sensing techniques included thermal infrared imagery in analog and digital form and conversion of data into thermograms. Delineation of geologic areas, surveys of vegetation and inventory of resources were also presented

Lexical innovation on the web and social media

This dissertation investigates the emergence and diffusion of English neologisms on the web and social media, employing a data-driven methodology to identify a substantial sample of 851 neologisms. Neologisms are examined from their coining to successful dissemination within the community, with the study revealing a wide spectrum of degrees of diffusion. The exploration extends to studying the usage and diffusion of selected neologisms on the web and on Twitter, with a particular focus on social dynamics and variation among different speaker groups. Moreover, the dissertation probes into semantic innovation, demonstrating substantial socio-semantic variation and polarized public discourse surrounding certain neologisms. The research conducts an extensive analysis of semantic innovation and socio-semantic variation, elucidating significant socio-semantic discrepancies between various communities. The dissertation sheds light on the social and semantic dynamics underpinning the life cycle of neologisms within a linguistically diverse community

Examining scientific thinking processes in open-ended serious games through gameplay data

Research on scientific problem-solving emphasizes the importance of problem solving and scientific inquiry as central components of the twenty-first century skills. Research has shown that open-ended serious games can facilitate students’ development of specific skills and improve learning performance through scientific problem-solving. However, understanding how students learn these complex skills in a game environment is a major challenge, as much research depends on typical paper-and-pencil assessments and self-reported surveys or other traditional observational and quantitative methods. The participants of the study were 237 sixth graders from two middle schools in the Southwestern area of the United States. The students used an open-ended serious game called Alien Rescue as their science curriculum for three weeks. The purpose of this study is, first, to identify students’ navigation behavior patterns in cognitive processes between at-risk and non-at-risk students within Alien Rescue. To accomplish this purpose, this study intends to use gameplay data by incorporating the integrated method of lag sequential analysis and sequential pattern mining together with a statistical analysis. The findings confirmed that the integrated method helped to explore students’ latent navigation behaviors as well as discover the differences of problem-solving processes between non-at-risk and at-risk students. The second purpose of this study is to examine the relationship between students’ learning performance and their scientific inquiry behaviors, which emerged as students engaged with Probe Design Center in this serious game. The results showed that the game metrics developed in Probe Design Center improved the predictions of both in-game and after-game performance. The cluster analyses with game metrics confirmed four unique groups regarding students’ scientific inquiry behaviors in Probe Design Center. This study concluded that the integrated methods of serious games analytics enabled researchers to investigate in-depth cognitive processes and scientific inquiry behaviors within a specific cognitive tool, Probe Design Center, and discover unique behavior groups across different school settings. The researcher identified the challenges of at-risk students in their cognitive processes and highlighted the support needs for these students. Consequently, this study proposed an interactive dashboard using the data-driven evidences to provide teachers just-in-time information to support students’ cognitive processes.Curriculum and Instructio

Aluminium Process Fault Detection and Diagnosis

The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation. Each element is explained together with examples of existing systems. A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data. A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators’ view of an industrial plant, so that it permits a situation-oriented action in real working environments