Extracting Causal Graphs from an Open Provenance Data Model

The open provenance architecture (OPA) approach to the challenge was distinct in several regards. In particular, it is based on an open, well-defined data model and architecture, allowing different components of the challenge workflow to independently record documentation, and for the workflow to be executed in any environment. Another noticeable feature is that we distinguish between the data recorded about what has occurred, emphprocess documentation, and the emphprovenance of a data item, which is all that caused the data item to be as it is and is obtained as the result of a query over process documentation. This distinction allows us to tailor the system to separately best address the requirements of recording and querying documentation. Other notable features include the explicit recording of causal relationships between both events and data items, an interaction-based world model, intensional definition of data items in queries rather than relying on explicit naming mechanisms, and emphstyling of documentation to support non-functional application requirements such as reducing storage costs or ensuring privacy of data. In this paper we describe how each of these features aid us in answering the challenge provenance queries.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Employment and earnings supplement, revised establishment data.

Mode of access: Internet.

Ranking of Michigan public high school districts by selected financial data /

Title from cover.

Hourly precipitation data:

Mode of access: Internet.

Local climatological data.

Mode of access: Internet.

Rainfall intensity-frequency data.

Contribution from Bureau of agricultural engineering.

Hydrologic data.

Mode of access: Internet.

Approximate processing of massive continuous quantile queries over high-speed data streams

Quantile computation has many applications including data mining and financial data analysis. It has been shown that an is an element of-approximate summary can be maintained so that, given a quantile query d (phi, is an element of), the data item at rank [phi N] may be approximately obtained within the rank error precision is an element of N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different phi and is an element of poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.

Space efficient quantile summary for constrained sliding windows on a data stream

In many online applications, we need to maintain quantile statistics for a sliding window on a data stream. The sliding windows in natural form are defined as the most recent N data items. In this paper, we study the problem of estimating quantiles over other types of sliding windows. We present a uniform framework to process quantile queries for time constrained and filter based sliding windows. Our algorithm makes one pass on the data stream and maintains an E-approximate summary. It uses O((1)/(epsilon2) log(2) epsilonN) space where N is the number of data items in the window. We extend this framework to further process generalized constrained sliding window queries and proved that our technique is applicable for flexible window settings. Our performance study indicates that the space required in practice is much less than the given theoretical bound and the algorithm supports high speed data streams.

Development of a cross-European virtual sample repository and HIV resistance database

BACKGROUND: Several European HIV observational data bases have, over the last decade, accumulated a substantial number of resistance test results and developed large sample repositories, There is a need to link these efforts together, We here describe the development of such a novel tool that allows to bind these data bases together in a distributed fashion for which the control and data remains with the cohorts rather than classic data mergers.METHODS: As proof-of-concept we entered two basic queries into the tool: available resistance tests and available samples. We asked for patients still alive after 1998-01-01, and between 180 and 195 cm of height, and how many samples or resistance tests there would be available for these patients, The queries were uploaded with the tool to a central web server from which each participating cohort downloaded the queries with the tool and ran them against their database, The numbers gathered were then submitted back to the server and we could accumulate the number of available samples and resistance tests.RESULTS: We obtained the following results from the cohorts on available samples/resistance test: EuResist: not availableI11,194; EuroSIDA: 20,71611,992; ICONA: 3,751/500; Rega: 302/302; SHCS: 53,78311,485, In total, 78,552 samples and 15,473 resistance tests were available amongst these five cohorts. Once these data items have been identified, it is trivial to generate lists of relevant samples that would be usefuI for ultra deep sequencing in addition to the already available resistance tests, Saon the tool will include small analysis packages that allow each cohort to pull a report on their cohort profile and also survey emerging resistance trends in their own cohort,CONCLUSIONS: We plan on providing this tool to all cohorts within the Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN) and will provide the tool free of charge to others for any non-commercial use, The potential of this tool is to ease collaborations, that is, in projects requiring data to speed up identification of novel resistance mutations by increasing the number of observations across multiple cohorts instead of awaiting single cohorts or studies to reach the critical number needed to address such issues.

Cooperative caching techniques for mobile ad hoc networks

Data caching can remarkably improve the efficiency of information access in a wireless ad hoc network by reducing the access latency and bandwidth usage. The cache placement problem minimizes total data access cost in ad hoc networks with multiple data items. The ad hoc networks are multi hop networks without a central base station and are resource constrained in terms of channel bandwidth and battery power. By data caching the communication cost can be reduced in terms of bandwidth as well as battery energy. As the network node has limited memory the problem of cache placement is a vital issue. This paper attempts to study the existing cooperative caching techniques and their suitability in mobile ad hoc networks.

Parallel pipelined histogram architectures

Proposed is a unique cell histogram architecture which will process k data items in parallel to compute 2q histogram bins per time step. An array of m/2q cells computes an m-bin histogram with a speed-up factor of k; k ⩾ 2 makes it faster than current dual-ported memory implementations. Furthermore, simple mechanisms for conflict-free storing of the histogram bins into an external memory array are discussed.

A parallel quantum histogram architecture

A parallel formulation of an algorithm for the histogram computation of n data items using an on-the-fly data decomposition and a novel quantum-like representation (QR) is developed. The QR transformation separates multiple data read operations from multiple bin update operations thereby making it easier to bind data items into their corresponding histogram bins. Under this model the steps required to compute the histogram is n/s + t steps, where s is a speedup factor and t is associated with pipeline latency. Here, we show that an overall speedup factor, s, is available for up to an eightfold acceleration. Our evaluation also shows that each one of these cells requires less area/time complexity compared to similar proposals found in the literature.