Computer Science

Abstract Distributed Shared Memory (DSM)[1] has become a very popular paradigm in distributed systems. A DSM is essentially a way of seamlessly sharing the physical memories of loosely connected systems. An implementation of a DSM can be categorized as page-based, shared-variable based or object based depending on the granularity of the data being shared. This paper discussed the design and implementation of an object based DSM running in user space.

Outlier detection is an important problem that has been researched within diverse knowledge disciplines and application domains. Many of these techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on outlier detection.

Outlier detection has been a very important concept in the realm of data analysis. Recently, several application domains have realized the direct mapping between outliers in data and real world anomalies, that are of great interest to an analyst. Outlier detection has been researched within various application domains and knowledge disciplines. This survey provides a comprehensive overview of existing outlier detection techniques by classifying them along different dimensions. Categories and Subject Descriptors: H.

Abstract Measuring similarity or distance between two entities is a key step for several data mining and knowledge discovery tasks. The notion of similarity for continuous data is relatively well-understood, but for categorical data, the similarity computation is not straightforward. Several data-driven similarity measures have been proposed in the literature to compute the similarity between two categorical data instances but their relative performance has not been evaluated.

ABSTRACT This thesis deals with the problem of anomaly detection for sequence data. Anomaly detection has been a widely researched problem in several application domains such as system health management, intrusion detection, health-care, bio-informatics, fraud detection, and mechanical fault detection. Traditional anomaly detection techniques analyze each data instance (as a univariate or multivariate record) independently, and ignore the sequential aspect of the data.

We describe our project that marries data mining together with Grid computing. Specifically, we focus on one data mining application-the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining based algorithms to address different aspects of cyber security including malicious activities such as denial-of-service (DoS) traffic, worms, policy violations and inside abuse. MINDS has shown great operational success in detecting network intrusions in several real deployments.

Abstract This survey attempts to provide a comprehensive and structured overview of the existing research for the problem of detecting anomalies in discrete/symbolic sequences. The objective is to provide a global understanding of the sequence anomaly detection problem and how existing techniques relate to each other. The key contribution of this survey is the classification of the existing research into three distinct categories, based on the problem formulation that they are trying to solve.

Abstract: The goal of the effort was to develop a comprehensive situational awareness analysis tool for discovery of intrusive behavior in information infrastructures and understanding anomalous network traffic. The University of Minnesota team has developed a comprehensive, multi-stage analysis framework which provides tools and analysis methodologies to aid cyber security analysts in improving the quality and productivity of their analyses.

Abstract In this paper, we formulate the problem of summarization of a data set of transactions with categorical attributes as an optimization problem involving two objective functions–compaction gain and information loss. We propose metrics to characterize the output of any summarization algorithm. We investigate two approaches to address this problem. The first approach is an adaptation of clustering and the second approach makes use of frequent itemsets from the association analysis domain.

Abstract Characterizing vegetation phenology is a highly significant problem, due to its importance in regulating ecosystem carbon cycling, interacting with climate changes, and decision-making of croplands managements. While ground based sensors, such as the AmeriFlux sensors, can provide measurements at high temporal resolution (every hour) and can be used to accurately calculate vegetation phenology indices, they are limited to only a few sites.

Abstract Reference based analysis (RBA) is a novel data mining tool for exploring a test data set with respect to a reference data set. The power of RBA lies in it ability to transform any complex data type, such as symbolic sequences and multi-variate categorical data instances, into a multivariate continuous representation.

ABSTRACT Gaussian process (GP) is increasingly becoming popular as a kernel machine learning tool for non-parametric data analysis. Recently, GP has been applied to model non-linear dependencies in time series data. GP based analysis can be used to solve problems of time series prediction, forecasting, missing data imputation, change point detection, anomaly detection, etc. But the use of GP to handle massive scientific time series data sets has been limited, owing to its expensive computational complexity.

One of the practical issues in clustering is the specification of the appropriate number of clusters, which is not obvious when analyzing geospatial datasets, partly because they are huge (both in size and spatial extent) and high dimensional. In this paper we present a computationally effcient model-based split and merge clustering algorithm that incrementally finds model parameters and the number of clusters.

Abstract Measuring similarity or distance between two entities is a key step for several data mining and knowledge discovery tasks. The notion of similarity for continuous data is relatively well-understood, but for categorical data, the similarity computation is not straightforward. Several data-driven similarity measures have been proposed in the literature to compute the similarity between two categorical data instances but their relative performance has not been evaluated.

Abstract Accurate damage assessment due to major natural and anthropogenic disasters is becoming critical due to increasing human and economic losses. This increase in loss of life and severe damages can be attributed to the growing population, and human migration and settlements in disaster prone regions of the world. Rapid damage assessment and dissemination of accurate information is critical for creating an effective emergency response.

Abstract Online time series change detection is a critical component of many monitoring systems, such as space and air-borne remote sensing instruments, cardiac monitors, and network traffic profilers, which continuously analyze observations recorded by sensors. Data collected by such sensors typically has a periodic component.