Concept-driven visualization for terascale data analytics

Over the past couple of decades the amount of scientific data sets has exploded. The science community has since been facing the common problem of being drowned in data, and yet starved of information. Identification and extraction of meaningful features from large data sets has become one of the central problems of scientific research, for both simulation as well as sensory data sets. The problems at hand are multifold and need to be addressed concurrently to provide scientists with the necessary tools, methods, and systems. Firstly, the underlying data structures and management need to be optimized for the kind of data most commonly used in scientific research, i.e. terascale time-varying, multi-dimensional, multi-variate, and potentially non-uniform grids. This implies avoidance of data duplication, utilization of a transparent query structure, and use of sophisticated underlying data structures and algorithms.Secondly, in the case of scientific data sets, simplistic queries are not a sufficient method to describe subsets or features. For time-varying data sets, many features can generally be described as local events, i.e. spatially and temporally limited regions with characteristic properties in value space. While most often scientists know quite well what they are looking for in a data set, at times they cannot formally or definitively describe their concept well to computer science experts, especially when based on partially substantiated knowledge. Scientists need to be enabled to query and extract such features or events directly and without having to rewrite their hypothesis into an inadequately simple query language. Thirdly, tools to analyze the quality and sensitivity of these event queries itself are required. Understanding local data sensitivity is a necessity for enabling scientists to refine query parameters as needed to produce more meaningful findings.Query sensitivity analysis can also be utilized to establish trends for event-driven queries, i.e. how does the query sensitivity differ between locations and over a series of data sets. In this dissertation, we present an approach to apply these interdependent measures to aid scientists in better understanding their data sets. An integrated system containing all of the above tools and system parts is presented

Interactive Feature Selection and Visualization for Large Observational Data

Data can create enormous values in both scientific and industrial fields, especially for access to new knowledge and inspiration of innovation. As the massive increases in computing power, data storage capacity, as well as capability of data generation and collection, the scientific research communities are confronting with a transformation of exploiting the advanced uses of the large-scale, complex, and high-resolution data sets in situation awareness and decision-making projects. To comprehensively analyze the big data problems requires the analyses aiming at various aspects which involves of effective selections of static and time-varying feature patterns that fulfills the interests of domain users. To fully utilize the benefits of the ever-growing size of data and computing power in real applications, we proposed a general feature analysis pipeline and an integrated system that is general, scalable, and reliable for interactive feature selection and visualization of large observational data for situation awareness. The great challenge tackled in this dissertation was about how to effectively identify and select meaningful features in a complex feature space. Our research efforts mainly included three aspects: 1. Enable domain users to better define their interests of analysis; 2. Accelerate the process of feature selection; 3. Comprehensively present the intermediate and final analysis results in a visualized way. For static feature selection, we developed a series of quantitative metrics that related the user interest with the spatio-temporal characteristics of features. For timevarying feature selection, we proposed the concept of generalized feature set and used a generalized time-varying feature to describe the selection interest. Additionally, we provided a scalable system framework that manages both data processing and interactive visualization, and effectively exploits the computation and analysis resources. The methods and the system design together actualized interactive feature selections from two representative large observational data sets with large spatial and temporal resolutions respectively. The final results supported the endeavors in applications of big data analysis regarding combining the statistical methods with high performance computing techniques to visualize real events interactively

Data-Driven Anomaly Detection in Industrial Networks

Since the conception of the first Programmable Logic Controllers (PLCs) in the 1960s, Industrial Control Systems (ICSs) have evolved vastly. From the primitive isolated setups, ICSs have become increasingly interconnected, slowly forming the complex networked environments, collectively known as Industrial Networks (INs), that we know today. Since ICSs are responsible for a wide range of physical processes, including those belonging to Critical Infrastructures (CIs), securing INs is vital for the well-being of modern societies. Out of the many research advances on the field, Anomaly Detection Systems (ADSs) play a prominent role. These systems monitor IN and/or ICS behavior to detect abnormal events, known or unknown. However, as the complexity of INs has increased, monitoring them in the search of anomalous trends has effectively become a Big Data problem. In other words, IN data has become too complex to process it by traditional means, due to its large scale, diversity and generation speeds. Nevertheless, ADSs designed for INs have not evolved at the same pace, and recent proposals are not designed to handle this data complexity, as they do not scale well or do not leverage the majority of the data types created in INs. This thesis aims to fill that gap, by presenting two main contributions: (i) a visual flow monitoring system and (ii) a multivariate ADS that is able to tackle data heterogeneity and to scale efficiently. For the flow monitor, we propose a system that, based on current flow data, builds security visualizations depicting network behavior while highlighting anomalies. For the multivariate ADS, we analyze the performance of Multivariate Statistical Process Control (MSPC) for detecting and diagnosing anomalies, and later we present a Big Data, MSPCinspired ADS that monitors field and network data to detect anomalies. The approaches are experimentally validated by building INs in test environments and analyzing the data created by them. Based on this necessity for conducting IN security research in a rigorous and reproducible environment, we also propose the design of a testbed that serves this purpose

AIOps for a Cloud Object Storage Service

With the growing reliance on the ubiquitous availability of IT systems and services, these systems become more global, scaled, and complex to operate. To maintain business viability, IT service providers must put in place reliable and cost efficient operations support. Artificial Intelligence for IT Operations (AIOps) is a promising technology for alleviating operational complexity of IT systems and services. AIOps platforms utilize big data, machine learning and other advanced analytics technologies to enhance IT operations with proactive actionable dynamic insight. In this paper we share our experience applying the AIOps approach to a production cloud object storage service to get actionable insights into system's behavior and health. We describe a real-life production cloud scale service and its operational data, present the AIOps platform we have created, and show how it has helped us resolving operational pain points.Comment: 5 page

GeoLens: enabling interactive visual analytics over large-scale, multidimensional geospatial datasets

2015 Spring.Includes bibliographical references.With the rapid increase of scientific data volumes, interactive tools that enable effective visual representation for scientists are needed. This is critical when scientists are manipulating voluminous datasets and especially when they need to explore datasets interactively to develop their hypotheses. In this paper, we present an interactive visual analytics framework, GeoLens. GeoLens provides fast and expressive interactions with voluminous geospatial datasets. We provide an expressive visual query evaluation scheme to support advanced interactive visual analytics technique, such as brushing and linking. To achieve this, we designed and developed the geohash based image tile generation algorithm that automatically adjusts the range of data to access based on the minimum acceptable size of the image tile. In addition, we have also designed an autonomous histogram generation algorithm that generates histograms of user-defined data subsets that do not have pre-computed data properties. Using our approach, applications can generate histograms of datasets containing millions of data points with sub-second latency. The work builds on our visual query coordinating scheme that evaluates geospatial query and orchestrates data aggregation in a distributed storage environment while preserving data locality and minimizing data movements. This paper includes empirical benchmarks of our framework encompassing a billion-file dataset published by the National Climactic Data Center