Seeking to understand students' differences, including their different levels of prior knowledge and potential barriers to learning, is an essential part of teaching. Adapting teaching in a responsive way, including by providing targeted support to students who are struggling, is likely to increase student success. In this study, we apply digital courseware in the teaching of the undergraduate discrete mathematics course. Digital courseware has improved the analytics available to faculty and increased personalized responses to students on designated topics. We developed a tool to collect data from digital courseware and allow instructors and TAs to visualize the performance of students from pre-class exercises, in-class quizzes, weekly assignments, collaborative projects, and exams. Based on the data, instructors and the TAs could divide students into groups and adjust classroom lectures and recitation sessions to better address each student groups' areas of deficiency.

Due to the continuous, unbounded, and dynamic characteristics of the streaming data, mining data streams becomes a very challenging task. When analyzing online data streams, it is necessary to produce accurate results in a very short amount of time. The parallel processing power of Graphics Processing Units (GPUs) can be used to accelerate the processing and produce results in a timely manner. In this paper, we present GStreamMiner, a GPU-accelerated data stream mining framework and demonstrate its application using outlier detection over continuous streaming data as a case study. The demo software provides a visual interface which is continuously get updated with new results as the data stream progresses. It also facilitates the users to compare the performance of the GPU and CPU versions of the outlier detection algorithm.

Outlier detection or anomaly detection is applied in numerous applications, such as fraud detection, network intrusion detection, manufacturing, and environmental monitoring. Due to the continuous and dynamic characteristics of streaming data, outlier detection over data streams becomes a very challenging task. When analyzing real-time data streams, it is typically impossible to store the entire set of data due to space limitations. Also due to the high data rates, it is necessary to produce the results in a limited amount of time. Parallel processing power of Graphics Processing Units (GPUs) can be used to accelerate the outlier detection process, and thus address the challenges of outlier detection over data streams. This paper proposes a GPU-accelerated outlier detection algorithm for continuous data streams using kernel density estimation approach. Experiments show that the proposed SOD_GPU algorithm is efficient for detecting outliers in high-dimensional, high-speed data streams, and produces results in a timely manner without compromising the outlier detection accuracy. The proposed method achieved up to 20X speedup compared to a respective multi-core CPU implementation, and the speedup increases with the number of data attributes and the input data rate.

This paper proposes a novel naïve Bayesian classifier in categorical uncertain data streams. Uncertainty in categorical data is usually represented by vector valued discrete pdf, which has to be carefully handled to guarantee the underlying performance in data mining applications. In this paper, we map the probabilistic attribute to deterministic points in the Euclidean space and design a distance based and a density based algorithms to measure the correlations between feature vectors and class labels. We also devise a new pre-binning approach to guarantee bounded computation and memory cost in uncertain data streams classification. Experimental results in real uncertain data streams prove that our density-based naive classifier is efficient, accurate, and robust to data uncertainty.

Recommendation systems are a popular marketing strategy for online service providers. These systems predict a customer's future preferences from the past behaviors of that customer and the other customers. Most of the popular online stores process millions of transactions per day; therefore, providing quick and quality recommendations using the large amount of data collected from past transactions can be challenging. Parallel processing power of GPUs can be used to accelerate the recommendation process. However, the amount of memory available on a GPU card is limited; thus, a number of passes may be required to completely process a large-scale dataset. This paper proposes two parallel, item-based recommendation algorithms implemented using the CUDA platform. Considering the high sparsity of the user-item data, we utilize two compression techniques to reduce the required number of passes and increase the speedup. The experimental results on synthetic and real-world datasets show that our algorithms outperform the respective CPU implementations and also the naïve GPU implementation which does not use compression.

SUMMARY Osteoporosis is characterized by reduced bone mass and debilitating fractures and is likely to reach epidemic proportions. Because of the vigorous research taking place in fields related to osteoporosis, bone biologists are overwhelmed by the amount of literature being generated on a regular basis. This problem can be alleviated by inferring and extracting novel relationships among biological entities appearing in the biological literature. With the development of large online publicly available databases of biological literature, such an approach becomes even more appealing. The novel relationships between biological terms thus discovered constitute new hypotheses that can be verified using experiments. This paper presents a novel method called multilevel text mining for the extraction of potentially meaningful biological relationships. Multilevel mining uses transitive maximum flow graph analysis coupled with set combination operations of union and intersection. Set operators are applied along and across the paths of a transitive flow graph to combine the data. In the first level of the multilevel mining process, protein domain names are used. Novel relationships between domains are extracted by the transitive text mining analysis. In the second level, these newly discovered relationships are used to extract relevant protein names. Set operators are used in various combinations to obtain different sets of results. Copyright © 2011 John Wiley & Sons, Ltd.

Osteoporosis, which is characterized by reduced bone mass and debilitating fractures, may reach epidemic proportions with the aging of the US population. The intensity of research in this field of study is reflected by the facts that The American Society of Bone and Mineral Research has a membership of nearly 4,000 physicians, clinical investigators, and basic research scientists from over fifty countries and that NIH is expected to spend over 200 million dollars on osteoporosis research alone in 2010. Bone biologists may be overwhelmed by the amount of literature constantly being generated, thus the identification and extraction of existing and novel relationships among biological entities or terms appearing in the biological literature is an ongoing problem. The problem has become more pressing with the development of large online publicly available databases of biological literature. Extraction and visualization of relationships between biological entities appearing in these databases offers the opportunity of keeping researchers up-to-date in their research domain. This may be achieved through helping them visualize possible biological pathways and by generating likely new hypotheses concerning novel interactions through methods such as transitive closure network flow. All generated predictions can be verified against already existing data, and possible new relationships can be verified against experiment. This paper presents a method for the extraction and visualization of potentially meaningful relationships.

Numerous monitoring applications such as traffic control systems, border patrol monitoring, and person locater services generate a large number of multimedia data streams that need to be analyzed and processed using image processing and data stream management techniques in order to detect significant events of interest or abnormal conditions. Such multimedia monitoring systems usually have high-bandwidth characteristics, stricter real-time deadlines, and high accuracy requirements. In an attempt to meet all of these requirements, we have designed Symbiote - a distributed Reconfigurable Logic Assisted multimedia Data Stream Management System (RLADSMS) at Indiana University Purdue University, Indianapolis (IUPUI) that provides hardware accelerated data stream processing using Field Programmable Gate Arrays (FPGAs).

Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, network latency, outdated sources and sampling errors. These kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper, we propose a new rule-based classification and prediction algorithm called uRule for classifying uncertain data. This algorithm introduces new measures for generating, pruning and optimizing rules. These new measures are computed considering uncertain data interval and probability distribution function. Based on the new measures, the optimal splitting attribute and splitting value can be identified and used for classification and prediction. The proposed uRule algorithm can process uncertainty in both numerical and categorical data. Our experimental results show that uRule has excellent performance even when data is highly uncertain.

Studies of gene expression in primary human disease tissue often span several years in order to achieve reasonably large sample sizes and to collect patient clinical information making this data particularly valuable. Due to the lack of a central repository, this data has only been available through disparate and non-publicly accessible sources following publication. We developed disease-to-gene expression mapper (D-GEM) as a publically accessible database and data mining toolbox for microarray data of human primary disease tissue. A statistical pipeline has also been implemented to identify genes over-expressed in disease tissue samples in comparison with normal control samples, or genes whose expression values are associated with clinical parameters such as patient survival rate. One potential application of this data is the identification of pathway specific cancer prognosis markers. By applying a novel, gene signatures for cancer prognosis in the context of known biological pathways in cancer development were identified and confirmed.

Many real datasets have uncertain categorical attribute values that are only approximately measured or imputed. Uncertainty in categorical data is commonplace in many applications, including biological annotation, medial diagnosis and automatic error detection. In such domains, the exact value of an attribute is often unknown, but may be estimated from a number of reasonable alternatives. Current conceptual clustering algorithms do not provide a convenient means for handling this type of uncertainty. In this paper we extend traditional conceptual clustering algorithm to explicitly handle uncertainty in data values. In this paper we propose new total utility (TU) index for measuring the quality of the clustering. And we develop improved algorithms for efficiently clustering uncertain categorical data, based on the COBWEB conceptual clustering algorithm. Experimental results using real datasets demonstrate how these algorithms and new TU measure can effectively improve the performance of clustering through the use of internal probabilistic information.

In many applications data values are inherently uncertain. This includes moving-objects, sensors and biological databases. There has been recent interest in the development of database management systems that can handle uncertain data. Some proposals for such systems include attribute values that are uncertain. In particular, an attribute value can be modeled as a range of possible values, associated with a probability density function. Previous efforts for this type of data have only addressed simple queries such as range and nearest-neighbor queries. Queries that join multiple relations have not been addressed in earlier work despite the significance of joins in databases. In this paper we address join queries over uncertain data. We propose a semantics for the join operation, define probabilistic operators over uncertain data, and propose join algorithms that provide efficient execution of probabilistic joins. The paper focuses on an important class of joins termed probabilistic threshold joins that avoid some of the semantic complexities of dealing with uncertain data. For this class of joins we develop three sets of optimization techniques: item-level, page-level, and index-level pruning. These techniques facilitate pruning with little space and time overhead, and are easily adapted to most join algorithms. We verify the performance of these techniques experimentally.

Index structures are designed to optimize search performance, while at the same time supporting efficient data updates. Although not explicit, existing index structures are typically based upon the assumption that the rate of updates will be small compared to the rate of querying. This assumption is not valid in streaming data environments such as sensor and moving object databases, where updates are received incessantly. In fact, for many applications, the rate of updates may well exceed the rate of querying. In such environments, index structures suffer from poor performance due to the large overhead of keeping the index updated with the latest data. Recent efforts at indexing moving object data assume objects move in a restrictive manner (e.g. in straight lines with constant velocity). In this paper, we propose an index structure explicitly designed to perform well for both querying and updating. We assume a more relaxed model of object movement. In particular, we observe that objects often stay in a region (e.g., building) for an extended amount of time, and exploit this phenomenon to optimize an index for both updates and queries. The paper is developed with the example of R-trees, but the ideas can be extended to other index structures as well. We present the design of the change tolerant R-tree, and an experimental evaluation.

Moving object environments contain large numbers of queries and continuously moving objects. Traditional spatial index structures do not work well in this environment because of the need to frequently update the index which results in very poor performance. In this paper, we present a novel indexing structure, namely the Q+Rtree, based on the observation that: i) most moving objects are in quasi-static state most of time, and ii) the moving patterns of objects are strongly related to the topography of the space. The Q+Rtree is a hybrid tree structure which consists of both an R*tree and a QuadTree. The R*tree component indexes quasi-static objects ie., those that are currently moving slowly and are often crowded together in buildings or houses. The Quadtree component indexes fast moving objects which are dispersed over wider regions. We also present the experimental evaluation of our approach.

Moving object environments are characterized by large numbers of moving objects and numerous concurrent continuous queries over these objects. Efficient evaluation of these queries in response to the movement of the objects is critical for supporting acceptable response times. In such environments, the traditional approach of building an index on the objects (data) suffers from the need for frequent updates and thereby results in poor performance. In fact, a brute force, no-index strategy yields better performance in many cases. Neither the traditional approach nor the brute force strategy achieve reasonable query processing times. This paper develops novel techniques for the efficient and scalable evaluation of multiple continuous queries on moving objects. Our solution leverages two complimentary techniques: Query Indexing and Velocity Constrained Indexing (VCI). Query Indexing relies on 1) incremental evaluation, 2) reversing the role of queries and data, and 3) exploiting the relative locations of objects and queries. VCI takes advantage of the maximum possible speed of objects in order to delay the expensive operation of updating an index to reflect the movement of objects. In contrast to an earlier technique that requires exact knowledge about the movement of the objects, VCI does not rely on such information. While Query Indexing outperforms VCI, it does not efficiently handle the arrival of new queries. Velocity constrained indexing, on the other hand, is unaffected by changes in queries. We demonstrate that a combination of Query Indexing and Velocity Constrained Indexing enables the scalable execution of insertion and deletion of queries in addition to processing ongoing queries. We also develop several optimizations and present a detailed experimental evaluation of our techniques. The experimental results show that the proposed schemes outperform the traditional approaches by almost two orders of magnitude.