In this Birds-of-a-Feather session, we aim to create a collaborative environment for people to explore, share ideas about project-based learning (PBL) and get started on developing and/or revising hands-on projects for their courses. We invite conversations about the need for project-based learning and its impact on students, the challenges of designing and deploying projects, and the value that projects add to the computer science (CS) curriculum. Our goal is multi-fold:

Technology advances and lower equipment costs are enabling non-invasive, convenient recording of brain data outside of clinical settings in more real-world environments, and by non-experts. Despite the growing interest in and availability of brain signal datasets, most analytical tools are made for experts in the specific device technology, and have rigid constraints on the type of analysis available. We developed BrainEx to support interactive exploration and discovery within brain signals datasets. BrainEx takes advantage of algorithms that enable fast exploration of complex, large collections of time series data, while being easy to use and learn. This system enables researchers to perform similarity search, explore feature data and natural clustering, and select sequences of interest for future searches and exploration, while also maintaining the usability of a visual tool. In addition to describing the distributed architecture and visual design for BrainEx, this paper reports on a benchmark experiment showing that it outperforms other existing systems for similarity search. Additionally, we report on a preliminary user study in which domain experts used the visual exploration interface and affirmed that it meets the requirements. Finally, it presents a case study using BrainEx to explore real-world, domain-relevant data.

Correlation-based analytics of time series data is important for a wide range of applications from stock analysis to weather forecasting. Yet performing these analytics at the granularity of subsequences of time series is prohibitively costly for large data sets. Our proposed framework CORAL (CORrelation based AnaLytics) tackles this challenge by adopting a preprocess-once and query-many-times paradigm. In a preprocessing step, CORAL compresses time series data into compact clusters, each identified by a unique representative, taking advantage of the Euclidean distance triangle inequality property. Using a mapping between the metric Euclidean distance and non-metric Pearson correlation, we establish inter- and intra-cluster correlation bounds as foundation for CORAL processing. In the CORAL model, inter-cluster correlation relationships are captured as a compact overlay graph with representatives as nodes and the correlation between cluster representatives as edges. The two CORAL bounds support time series matching by realizing the comparison among the cluster representatives at the abstract level in the CORAL graph model in place of the underlying huge collection of raw time series. The resulting CORAL model effectively supports a rich set of analytics operations including retrieval of the best-correlated subsequence, self-correlation, and detection of groups of correlated subsequences. Our comprehensive experimental evaluation on 85 real benchmark datasets demonstrates that CORAL is many fold faster than state-of-the-art systems while returning highly accurate results.

Metal powders are used as feedstock in a variety of manufacturing methods, wherein the metallic particulates may be deposited upon a substrate, spread upon a build plate, or melted layer-by-layer. Regardless of how the powder is consumed during a given manufacturing process, the ease with which a powder flows maintains a significant impact on the properties and behavior of the resultant component procured. This is especially true for cold spray additive manufacturing, which is a solid-state metallic particulate deposition process that requires suitable flowability to prevent component waviness as well as prevent nozzle clogging.That being said, particle-level geometrical properties, such as size and shape features, greatly impact the capacity of a powder to flow, or its flowability. Existing techniques for measuring a particle's flowability require manual powder processing which can be both time-consuming and vulnerable to man-made error. Both of these setbacks can be addressed through automatic, data-driven techniques trained to classify flowability from particle property measurements and expertly procured powder Hall Flow rates for training and testing.To our knowledge, we present the first computational model for classifying flowability (when captured in terms of the industrially standardized Hall Flow rates) of a metal powder given its particle-level physical property measurements. After obtaining twenty-one metal powder samples from manufacturers, particle-level physical property measurements of each powder were measured. We used these particle measurements to train a Decision Tree model to classify whether a particle's Hall Flow rate was fast or slow. With an achieved accuracy 98.04%, our work illustrates that powder flowability, through the lens of Hall Flow rates, can be accurately classified using data-driven techniques. This work lays a promising foundation for further exploration into predictive models for this cutting-edge domain.

Continuous growth in sensor data and other temporal sequence data necessitates efficient retrieval and similarity search support on these big time series datasets. However, finding exact similarity results, especially at the granularity of subsequences, is known to be prohibitively costly for large data sets. In this paper, we thus propose an efficient framework for solving this exact subsequence similarity match problem, called TINN (TIme series Nearest Neighbor search). Exploiting the range interval diversity properties of time series datasets, TINN captures similarity at two levels of abstraction, namely, relationships among subsequences within each long time series and relationships across distinct time series in the data set. These relationships are compactly organized in an augmented relationship graph model, with the former relationships encoded in similarity vectors at TINN nodes and the later captured by augmented edge types in the TINN Graph. Query processing strategy deploy novel pruning techniques on the TINN Graph, including node skipping, vertical and horizontal pruning, to significantly reduce the number of time series as well as subsequences to be explored. Comprehensive experiments on synthetic and real world time series data demonstrate that our TINN model consistently outperforms state-of-the-art approaches while still guaranteeing to retrieve exact matches.

The World Wide Web provides an excellent platform for investors to discover new partnership opportunities with a variety of companies. Analysts can categorize websites according to their business domains to retain relevant investment opportunities. Classifying websites manually is too expensive and time-consuming; thus, automatic classification tools are necessary. In this paper, we present FinDX (Financial Data EXploration), a tool for automatic website content classification for the financial technology (fintech) domain. At the core of our system is a keyword-based web crawler that extracts text from the landing page and relevant subpages, such as the About or Product pages of company websites. After cleaning the text and filtering it using part-of-speech tagging, we use a Linear Support Vector Machine (SVM) or Multilayer Perceptron (MLP) to classify a company website as fintech or non-fintech. FinDX achieves high binary classification accuracy on two different datasets of business websites, attaining a maximal F-score of 96%. In addition, our flexible tool is easily adaptable to any business domain and is not resource-expensive. This makes FinDX ideal for use in startup environments.

Domain-specific distances preferred by analysts for exploring similarities among time series tend to be "point-to-point" distances. Unfortunately, this point-wise nature limits their ability to perform meaningful comparisons between sequences of different lengths and with temporal mis-alignments. Analysts instead need "elastic" alignment tools such as Dynamic Time Warping (DTW) to perform such flexible comparisons. However, the existing alignment tools are limited in that they do not incorporate diverse distances. To address this shortcoming, our work introduces the first conceptual framework called Generalized Dynamic Time Warping (GDTW) that supports now alignment (warping) of a large array of domain-specific distances in a uniform manner. While the classic DTW and its prior extensions focus on the Euclidean Distance, our GDTW is the first method that generalizes the ubiquitous DTW and "extends" its warping capabilities to a rich diversity of point-to-point distances. Based on our GDTW paradigm that preserves the efficiency of the dynamic programming paradigm of DTW, we design an abstraction that implemented by our GDTW Design Tool enables analysts to "warp" new distances with little programming effort. Through extensive evaluation studies on 85 real public domain benchmark datasets, we show that our newly warped distances offer higher classification accuracy than the previously available distances for the majority of these datasets. Further, our case study on heart arrhythmia data illustrates the utility of the new distances enabled by our GDTW warping methodology.

Similarity analytics of time series data are critical for a wide range of applications ranging from medical to financial, and from weather forecasting to image processing. Yet these analytics tasks are known to be prohibitively expensive for large data sets, especially when accounting for varying temporal alignments and lengths. Our proposed framework tackles this challenge by adopting a preprocess-once and query-many-times paradigm. We extend a previous formal model interleaving the inexpensive Euclidean distance with the robust Dynamic Time Warping (DTW) to retrieve the k most similar matches to a given sample sequence. Our extended ONline EXploration of top k time series similarity system (K-ONEX) first encodes similarity relationships by compressing the raw time series into Euclidean-based groups; these groups are further explored using the elastic DTW to find similar sequences of any length and temporal alignment with response times that are almost as fast as retrieving only one best match. Our empirical results illustrate that K-ONEX provides response times that are 2-3 orders of magnitude faster than the benchmark and state-of-the-art methods while achieving 100% accuracy by exploring less than 0.5% of the sequences in each dataset.

Modern applications in this digital age collect a staggering amount of time series data from economic growth rates to electrical household consumption habits. To make sense of it, domain analysts interactively sift through these time series collections in search of critical relationships between and recurring patterns within these time series. The ONEX (Online Exploration of Time Series) system supports effective exploratory analysis of time series collections composed of heterogeneous, variable-length and misaligned time series using robust alignment dynamic time warping (DTW) methods. To assure real-time responsiveness even for these complex and compute-intensive analytics, ONEX precomputes and then encodes time series relationships based on the inexpensive-to-compute Euclidean distance into the ONEX base. Thereafter, based on a solid formal foundation, ONEX uses DTW-enhanced analytics to correctly extract relevant time series matches on this Euclidean-prepared ONEX base. Our live interactive demonstration shows how our ONEX exploratory tool, supported by a rich array of visual interactions and expressive visualizations, enables efficient mining and interpretation of the MATTERS real data collection composed of economic, social, and education data trends across the fifty American states.

Finding similar trends among time series data is critical for applications ranging from financial planning to policy making. The detection of these multifaceted relationships, especially time warped matching of time series of different lengths and alignments is prohibitively expensive to compute. To achieve real time responsiveness on large time series datasets, we propose a novel paradigm called Online Exploration of Time Series (ONEX) employing a powerful one-time preprocessing step that encodes critical similarity relationships to support subsequent rapid data exploration. Since the encoding of a huge number of pairwise similarity relationships for all variable lengths time series segments is not feasible, our work rests on the important insight that clustering with inexpensive point-to-point distances such as the Euclidean Distance can support subsequent time warped matching. Our ONEX framework overcomes the prohibitive computational costs associated with a more robust elastic distance namely the DTW by applying it over the surprisingly compact knowledge base instead of the raw data. Our comparative study reveals that ONEX is up to 19% more accurate and several times faster than the state-of-the-art. Beyond being a highly accurate and fast domain independent solution, ONEX offers a truly interactive exploration experience supporting novel time series operations.