An increasing number of distributed applications aim to provide services to users by interacting with a correspondingly growing set of data-intensive network services. Such applications, as well as the services they utilize, are generally expected to handle dynamically varying demands on resources and to run in large, heterogeneous, and dynamic environments, where the availability of resources ...

Introduction Taking into account reporting delays in surveillance systems is not methodologically trivial. Consequently, most use the date of the reception of data, rather than the (often unknown) date of the health event itself. The main drawback of this approach is the resulting reduction in sensitivity and specificity1. Combining syndromic data from multiple data streams (most health events ...

In this paper, we examine the problem of clustering massive graph streams. Graph clustering poses significant challenges because of the complex structures which may be present in the underlying data. The massive size of the underlying graph makes explicit structural enumeration very difficult. Consequently, most techniques for clustering multidimensional data are difficult to generalize to the ...

REGULAR PROGRAMMING OVER DATA STREAMS

I develop an asset-pricing model in which financial assets are valued for their liquidity– the extent to which they are useful in facilitating exchange–as well as for being claims to streams of consumption goods. The implications for average asset returns, the equitypremium puzzle, and the risk-free rate puzzle, are explored both analytically and quantitatively in a version of the model that ne...

Background In this article, we analyze one case instance of how proposals for change to the priority setting and resource allocation (PSRA) processes at a Canadian healthcare institution reached the decision agenda of the organization’s senior leadership. We adopt key concepts from an established policy studies framework – Kingdon’s multiple streams theory – to inform our analysis.   Methods Tw...

Clustering data streams is an important task in data mining research. Recently, some algorithms have been proposed to cluster data streams as a whole, but just few of them deal with multivariate data streams. Even so, these algorithms merely aggregate the attributes without touching upon the correlation among them. In order to overcome this issue, we propose a new framework to cluster multivari...

We propose SCAN, an innovative, spectral analysis framework for internet scale monitoring of multi-relational social media data, encoded in the form of tensor streams. In particular, a significant challenge is to detect key changes in the social media data, which could reflect important events in the real world, sufficiently quickly. Social media data have three challenging characteristics. Fir...

1. Nutrient diffusing substrata were used to determine the effect of added inorganic nitrogen (N) and phosphorus (P) on the development of epilithic and epixylic biofilms in 10 North American streams. Four treatments of diffusing substrata were used: Control (agar only), N addition (0.5 M NaNO3), P addition (0.5 M KH2PO4), and N + P combined (0.5 M NaNO3 + 0.5 M KH2PO4). Agar surfaces were cove...

Multimedia applications nowadays are becoming the standard, for they utilize the enormous human brain computational power. In the past, the Relational Database Model was generalized to the Multimedia Databases Model. Orthogonally, the Relational Database model was recently generalized to the Data Streams Model, as the technology advanced and Data became bulky and unbounded in size, utilizing se...