Graph signatures for visual analytics

We present a visual analytics technique to explore graphs using the concept of a data signature. A data signature, in our context, is a multidimensional vector that captures the local topology information surrounding each graph node. Signature vectors extracted from a graph are projected onto a low-dimensional scatterplot through the use of scaling. The resultant scatterplot, which reflects the similarities of the vectors, allows analysts to examine the graph structures and their corresponding real-life interpretations through repeated use of brushing and linking between the two visualizations. The interpretation of the graph structures is based on the outcomes of multiple participatory analysis sessions with intelligence analysts conducted by the authors at the Pacific Northwest National Laboratory. The paper first uses three public domain data sets with either well-known or obvious features to explain the rationale of our design and illustrate its results. More advanced examples are then used in a customized usability study to evaluate the effectiveness and efficiency of our approach. The study results reveal not only the limitations and weaknesses of the traditional approach based solely on graph visualization, but also the advantages and strengths of our signature-guided approach presented in the paper     

Visual analytics for spatiotemporal events

Multimedia Tools and Applications - Crimes, forest fires, accidents, infectious diseases, or human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For...     

Multistate analytics for continuous processes

Batch process monitoring methods, such as multiway PCA and multiblock multiway PLS, make use of process variable time profiles to normalize and define most likelihood trajectories for statistical process control. Nevertheless, a continuous process analytics counterpart has not been developed, nor addressed in the literature. This paper presents a novel methodology that defines “state variables” to determine the multiple operating points around which a continuous process operates. In this manner, the operating region is divided into multiple regions (states) and shifts in operating conditions are captured by such state variables. Transition trajectories between states are calculated to determine the most likely path from one state to another. This methodology is referred as multistate analytics and can be implemented in the context of empirical monitoring methods, named multistate PLS and multistate PCA. A case study using data from carbon dioxide removal process shows that multistate analytics is beneficial for statistical monitoring of continuous processes.     

Archetypoid analysis for sports analytics

We intend to understand the growing amount of sports performance data by finding extreme data points, which makes human interpretation easier. In archetypoid analysis each datum is expressed as a mixture of actual observations (archetypoids). Therefore, it allows us to identify not only extreme athletes and teams, but also the composition of other athletes (or teams) according to the archetypoid athletes, and to establish a ranking. The utility of archetypoids in sports is illustrated with basketball and soccer data in three scenarios. Firstly, with multivariate data, where they are compared with other alternatives, showing their best results. Secondly, despite the fact that functional data are common in sports (time series or trajectories), functional data analysis has not been exploited until now, due to the sparseness of functions. In the second scenario, we extend archetypoid analysis for sparse functional data, furthermore showing the potential of functional data analysis in sports analytics. Finally, in the third scenario, features are not available, so we use proximities. We extend archetypoid analysis when asymmetric relations are present in data. This study provides information that will provide valuable knowledge about player/team/league performance so that we can analyze athlete’s careers.     

Big data analytics for video surveillance

This article addresses the usage and scope of Big Data Analytics in video surveillance and its potential application areas. The current age of technology provides the users, ample opportunity to generate data at every instant of time. Thus in general, a tremendous amount of data is generated every instant throughout the world. Among them, amount of video data generated is having a major share. Education, healthcare, tours and travels, food and culture, geographical exploration, agriculture, safety and security, entertainment etc., are the key areas where a tremendous amount of video data is generated every day. A major share among it are taken by the daily used surveillance data captured from the security purpose camera and are recorded everyday. Storage, retrieval, processing, and analysis of such gigantic data require some specific platform. Big Data Analytics is such a platform, which eases this analysis task. The aim of this article is to investigate the current trends in video surveillance and its applications using Big Data Analytics. It also aims to focus on the research opportunities for visual surveillance in Big Data frameworks. We have reported here the state-of-the-art surveillance schemes for four different imaging modalities: conventional video scene, remotely sensed video, medical diagnostics, and underwater surveillance. Several works were reported in this research field over recent years and are categorized based on the challenges solved by the researchers. A list of tools used for video surveillance using Big Data framework is presented. Finally, research gaps in this domain are discussed.

     

Predictive analytics for delivering prevention services

Early diagnosis and prevention of problematic behaviors in youth play an important role in reducing treatment costs and decreasing the toll of antisocial behavior. Over the last several years, the science of preventing antisocial behavior in youth has made significant strides, with the development of evidence-based prevention programs (EBP) using randomized clinical trials. In this paper, we use a real case implemented by schools in an urban school district of 80,000 students in a mid-Atlantic state to show how predictive analytics can help to improve the quality of prevention programs and reduce the cost of delivering associated services. Data patterns are extracted from conduct disorder assessments using the Teacher Observation of Classroom Adaptation (TOCA) screening instrument and evaluated using the results of the Diagnostic Interview Schedule for Children (DISC). A mathematical method called Logical Analysis of Data (LAD) is used to analyze data patterns. Experimental results show that up to 91.58% of the cost of administering DISC would be saved by correctly identifying participants without conduct disorder and excluding them from the DISC test.     

Intelligent analytics for big video data

Multimedia Tools and Applications -     

Polyether ether ketone microstructures for chemical analytics

This paper presents an initial approach towards polyether ether ketone (PEEK) microfluidic structures for chemical analysis by introducing PEEK capillary electrophoresis (CE) chips. PEEK shows outstanding material features, such as extremely high chemical resistance and mechanical stability, high temperature resistance, very low adsorption. However, difficulties in PEEK processing have limited its application until now. Here, a new plasma enhanced thermal bonding process is introduced and resulting features of the microstructures are investigated.     

Quality-optimized predictive analytics

On-line statistical and machine learning analytic tasks over large-scale contextual data streams coming from e.g., wireless sensor networks, Internet of Things environments, have gained high popularity nowadays due to their significance in knowledge extraction, regression and classification tasks, and, more generally, in making sense from large-scale streaming data. The quality of the received contextual information, however, impacts predictive analytics tasks especially when dealing with uncertain data, outliers data, and data containing missing values. Low quality of received contextual data significantly spoils the progressive inference and on-line statistical reasoning tasks, thus, bias is introduced in the induced knowledge, e.g., classification and decision making. To alleviate such situation, which is not so rare in real time contextual information processing systems, we propose a progressive time-optimized data quality-aware mechanism, which attempts to deliver contextual information of high quality to predictive analytics engines by progressively introducing a certain controlled delay. Such a mechanism progressively delivers high quality data as much as possible, thus eliminating possible biases in knowledge extraction and predictive analysis tasks. We propose an analytical model for this mechanism and show the benefits stem from this approach through comprehensive experimental evaluation and comparative assessment with quality-unaware methods over real sensory multivariate contextual data.     

Video traffic analytics for large scale surveillance

The video traffic analysis is the most important issue for large scale surveillance. In the large scale surveillance system, huge amount of live digital video data is submitted to the storage servers through the number of externally connected scalable components. The system also contains huge amount of popular and unpopular old videos in the archived storage servers. The video data is delivered to the viewers, partly or completely on demand through a compact system. In real time, huge amount of video data is imported to the viewer’s node for various analysis purposes. The viewers use a number of interactive operations during the real time tracking suspect. The compact video on demand system is used in peer to peer mesh type hybrid architecture. The chunk of video objects move fast through the real time generated compact topological space. Video traffic analytics is required to transfer compressed multimedia data efficiently. In this work, we present a dynamically developed topological space, using mixed strategy by game approach to move the video traffic faster. The simulation results are well addressed in real life scenario.     

The Stratosphere platform for big data analytics

We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Stratosphere’s features include “in situ” data processing, a declarative query language, treatment of user-defined functions as first-class citizens, automatic program parallelization and optimization, support for iterative programs, and a scalable and efficient execution engine. Stratosphere covers a variety of “Big Data” use cases, such as data warehousing, information extraction and integration, data cleansing, graph analysis, and statistical analysis applications. In this paper, we present the overall system architecture design decisions, introduce Stratosphere through example queries, and then dive into the internal workings of the system’s components that relate to extensibility, programming model, optimization, and query execution. We experimentally compare Stratosphere against popular open-source alternatives, and we conclude with a research outlook for the next years.     

Pipeline provenance for cloud-based big data analytics

Provenance is information about the origin and creation of data. In data science and engineering related with cloud environment, such information is useful and sometimes even critical. In data analytics, it is necessary for making data-driven decisions to trace back history and reproduce final or intermediate results, even to tune models and adjust parameters in a real-time fashion. Particularly, in cloud, users need to evaluate data and pipeline trustworthiness. In this paper, we propose a solution: LogProv, toward realizing these functionalities for big data provenance, which needs to renovate data pipelines or some of big data software infrastructure to generate structured logs for pipeline events, and then stores data and logs separately in cloud space. The data are explicitly linked to the logs, which implicitly record pipeline semantics. Semantic information can be retrieved from the logs easily since they are well defined and structured beforehand. We implemented and deployed LogProv in Nectar Cloud,^* associated with Apache Pig, Hadoop ecosystem, and adopted Elasticsearch to provide query service. LogProv was evaluated and empirically case studied. The results show that LogProv is efficient since the performance overhead is no more than 10%; the query can be responded within 1 second; the trustworthiness is marked clearly; and there is no impact on the data processing logic of original pipelines.     

Gait design for an ice skating humanoid robot

Basic walking gaits are a common building block for many activities in humanoid robotics, such as robotic soccer. The nature of the walking surface itself also has a strong affect on an appropriate gait. Much work is currently underway in improving humanoid walking gaits by dealing with sloping, debris-filled, or otherwise unstable surfaces. Travel on slippery surfaces such as ice, for example, greatly increases the potential speed of a human, but reduces stability. Humans can compensate for this lack of stability through the adaptation of footwear such as skates, and the development of gaits that allow fast but controlled travel on such footwear.This paper describes the development of a gait to allow a small humanoid robot to propel itself on ice skates across a smooth surface, and includes work with both ice skates and inline skates. The new gait described in this paper relies entirely on motion in the frontal plane to propel the robot, and allows the robot to traverse indoor and outdoor ice surfaces more stably than a classic inverted pendulum-based walking gait when using the same skates. This work is demonstrated using Jennifer, a modified Robotis DARwIn-OP humanoid robot with 20 degrees of freedom.     

Multi-source data analytics for AM energy consumption prediction

The issue of Additive Manufacturing (AM) system energy consumption attracts increasing attention when many AM systems are applied in digital manufacturing systems. Prediction and reduction of the AM energy consumption have been established as one of the most crucial research targets. However, the energy consumption is related to many attributes in different components of an AM system, which are represented as multiple source data. These multi-source data are difficult to integrate and to model for AM energy consumption due to its complexity. The purpose of this study is to establish an energy value predictive model through a data-driven approach. Owing to the fact that multi-source data of an AM system involves nested hierarchy, a hybrid approach is proposed to tackle the issue. This hybrid approach incorporates clustering techniques and deep learning to integrate the multi-source data that is collected using the Internet of Things (IoT), and then to build the energy consumption prediction model for AM systems. This study aims to optimise the AM system by exploiting energy consumption information. An experimental study using the energy consumption data of a real AM system shows the merits of the proposed approach. Results derived using this hybrid approach reveal that it outperforms pre-existing approaches.     

Visual analytics and rendering for tunnel crack analysis

The visual analysis of surface cracks plays an essential role in tunnel maintenance when assessing the condition of a tunnel. To identify patterns of cracks, which endanger the structural integrity of its concrete surface, analysts need an integrated solution for visual analysis of geometric and multivariate data to decide if issuing a repair project is necessary. The primary contribution of this work is a design study, supporting tunnel crack analysis by tightly integrating geometric and attribute views to allow users a holistic visual analysis of geometric representations and multivariate attributes. Our secondary contribution is Visual Analytics and Rendering, a methodological approach which addresses challenges and recurring design questions in integrated systems. We evaluated the tunnel crack analysis solution in informal feedback sessions with experts from tunnel maintenance and surveying. We substantiated the derived methodology by providing guidelines and linking it to examples from the literature.     

A visual analytics agenda

Researchers have made significant progress in disciplines such as scientific and information visualization, statistically based exploratory and confirmatory analysis, data and knowledge representations, and perceptual and cognitive sciences. Although some research is being done in this area, the pace at which new technologies and technical talents are becoming available is far too slow to meet the urgent need. National Visualization and Analytics Center's goal is to advance the state of the science to enable analysts to detect the expected and discover the unexpected from massive and dynamic information streams and databases consisting of data of multiple types and from multiple sources, even though the data are often conflicting and incomplete. Visual analytics is a multidisciplinary field that includes the following focus areas: (i) analytical reasoning techniques, (ii) visual representations and interaction techniques, (iii) data representations and transformations, (iv) techniques to support production, presentation, and dissemination of analytical results. The R&D agenda for visual analytics addresses technical needs for each of these focus areas, as well as recommendations for speeding the movement of promising technologies into practice. This article provides only the concise summary of the R&D agenda. We encourage reading, discussion, and debate as well as active innovation toward the agenda for visual analysis.     

Scalable aggregation predictive analytics

We introduce a predictive modeling solution that provides high quality predictive analytics over aggregation queries in Big Data environments. Our predictive methodology is generally applicable in environments in which large-scale data owners may or may not restrict access to their data and allow only aggregation operators like COUNT to be executed over their data. In this context, our methodology is based on historical queries and their answers to accurately predict ad-hoc queries’ answers. We focus on the widely used set-cardinality, i.e., COUNT, aggregation query, as COUNT is a fundamental operator for both internal data system optimizations and for aggregation-oriented data exploration and predictive analytics. We contribute a novel, query-driven Machine Learning (ML) model whose goals are to: (i) learn the query-answer space from past issued queries, (ii) associate the query space with local linear regression & associative function estimators, (iii) define query similarity, and (iv) predict the cardinality of the answer set of unseen incoming queries, referred to the Set Cardinality Prediction (SCP) problem. Our ML model incorporates incremental ML algorithms for ensuring high quality prediction results. The significance of contribution lies in that it (i) is the only query-driven solution applicable over general Big Data environments, which include restricted-access data, (ii) offers incremental learning adjusted for arriving ad-hoc queries, which is well suited for query-driven data exploration, and (iii) offers a performance (in terms of scalability, SCP accuracy, processing time, and memory requirements) that is superior to data-centric approaches. We provide a comprehensive performance evaluation of our model evaluating its sensitivity, scalability and efficiency for quality predictive analytics. In addition, we report on the development and incorporation of our ML model in Spark showing its superior performance compared to the Spark’s COUNT method.