Panayiotis Neophytou

Global Transcriptional Response to Spermine, a Component of the Intramacrophage Environment, Reveals Regulation of Francisella Gene Expression through Insertion Sequence Elements

Tularemia is caused by the category A biodefense agent Francisella tularensis. This bacterium is associated with diverse environments and a plethora of arthropod and mammalian hosts. How F. tularensis adapts to these different conditions, particularly the eukaryotic intracellular environment in which it replicates, is poorly understood. Here, we demonstrate that the polyamines spermine and spermidine are environmental signals that alter bacterial stimulation of host cells. Genomewide analysis showed that F. tularensis LVS undergoes considerable changes in gene expression in response to spermine. Unexpectedly, analysis of gene expression showed that multiple members of two classes of Francisella insertion sequence (IS) elements, ISFtu1 and ISFtu2, and the genes adjacent to these elements were induced by spermine. Spermine was sufficient to activate transcription of these IS elements and of nearby genes in broth culture and in macrophages. Importantly, the virulent strain of F. tularensis, Schu S4, exhibited similar phenotypes of cytokine induction and gene regulation in response to spermine. Distinctions in gene expression changes between Schu S4 and LVS at one orthologous locus, however, correlated with differences in IS element location. Our results indicate that spermine and spermidine are novel triggers to alert F. tularensis of its eukaryotic host environment. The results reported here also identify an unexpected mechanism of gene regulation controlled by a spermine-responsive promoter contained within IS elements. Different arrangements of these mobile genetic elements among Francisella strains may contribute to virulence by conveying new expression patterns for genes from different strains.

Admission control mechanisms for continuous queries in the cloud

Amazon, Google, and IBM now sell cloud computing services.We consider the setting of a for-profit business selling data stream monitoring/management services and we investigate auction-based mechanisms for admission control of continuous queries. When submitting a query, each user also submits a bid of how much she is willing to pay for that query to run. The admission control auction mechanism then determines which queries to admit, and how much to charge each user in a way that maximizes system revenue while being strategyproof and sybil immune, incentivizing users to use the system honestly. Specifically, we require that each user maximizes her payoff by bidding her true value of having her query run. We design several payment mechanisms and experimentally evaluate them. We describe the provable game theoretic characteristics of each mechanism alongside its performance with respect to maximizing profit and total user payoff.

Class-based continuous query scheduling for data streams

Wireless sensor networks link the physical and digital worlds enabling both surveillance as well as scientific exploration. In both cases, on-line detection of interesting events can be accomplished with continuous queries (CQs) in a Data Stream Management System (DSMS). However, the quality-of-service requirements of detecting these events are different for different monitoring applications. The CQs for detecting anomalous events (e.g., fire, flood) have stricter response time requirements over CQs which are for logging and keeping statistical information of physical phenomena. In this work, we are proposing the Continuous Query Class (CQC) scheduler, a new scheduling policy which employs two-level scheduling that is able to handle different ranks of CQ classes. It provides the lowest response times for classes of critical CQs, while at the same time keeping reasonable response times for the other classes down the rank. We have implemented CQC in the AQSIOS prototype DSMS and evaluated it against existing scheduling policies under different workloads.

Towards Continuous Workflow Enactment Systems

Traditional workflow enactment systems and workflow design processes view the workflow as a one-time interaction with the various data sources, executing a series of steps once, whenever the workflow results are requested. The fundamental underlying assumption has been that data sources are passive and all interactions are structured along the request/reply (query) model. Hence, traditional Workflow Management Systems cannot effectively support business or scientific monitoring applications that require the processing of data streams. In this paper, we propose a paradigm shift from the traditional step-wise workflow execution model to a continuous execution model, in order to handle data streams published and delivered asynchronously from multiple sources.

CONFLuEnCE: CONtinuous workFLow ExeCution Engine

Traditional workflow enactment systems view a workflow as a one-time interaction with various data sources, executing a series of steps once, whenever the workflow results are requested. The fundamental underlying assumption has been that data sources are passive and all interactions are structured along the request/reply (query) model. Hence, traditional Workflow Management Systems cannot effectively support business or scientific reactive applications that require the processing of continuous data streams. In this demo, we will present our prototype which transforms workflow execution from the traditional step-wise workflow execution model to a continuous execution model, in order to handle data streams published and delivered asynchronously from multiple sources. We will demonstrate a supply chain management scenario which takes advantage of our continuous execution model to enable on-line interaction between different user roles as well as streaming data coming from various sources.

A continuous workflow scheduling framework

Traditional workflow management or enactment systems (WfMS) and workflow design processes view the workflow as a one-time interaction with the various data sources, i.e., when a workflow is invoked, its steps are executed once and in-order. The fundamental underlying assumption has been that data sources are passive and all interactions are structured along the request/reply (query) model. Hence, traditional WfMS cannot effectively support business or scientific monitoring applications that require the processing of data streams such as those generated nowadays by sensing devices as well as mobile and web applications. Our hypothesis is that WfMS, both in the scientific and business domains, can be extended to support data stream semantics to enable monitoring applications. This includes the ability to apply flexible bounds on unbounded data streams and the ability to facilitate on-the-fly processing of bounded bundles of data (window semantics). In our previous work we have developed and implemented a Continuous Workflow Model that supports our hypothesis. This implementation of a CONtinuous workFLow ExeCution Engine (CONFLuEnCE) led to the realization that different applications have different performance requirements and hence an integrated workflow scheduling framework is essential. Such a framework is the main contribution of this paper. In particular, we designed and implemented STAFiLOS, a STreAm FLOw Scheduling for Continuous Workflows framework within CONFLuEnCE and evaluated STAFiLOS based on the Linear Road Benchmark.

Visualization of Energy Consumption of Continuous Query Processing with Mobile Clients

Complex event detection over data streams has become ubiquitous through the widespread use of sensors, wireless connectivity and the wide variety of end-user mobile devices. Typically, event detection is carried out by a central server executing continuous queries. In this demonstration, we focus on the case where users with mobile devices submit continuous queries (for event detection) to a data stream management server which disseminates the results to the users over a shared broadcast medium. In order to minimize the overall energy consumption of the mobile devices (clients), we have proposed operator placement algorithms that split the processing of each continuous query between the centralized server and the requesting mobile clients, thus trading off energy consumption for communication energy consumption for computation. Specifically, in this demonstration, we present an interactive graphical interface to the inner workings of our three proposed operator placement algorithms, whereby attendees are able to investigate various query plans and the decisions that the algorithms make, as well as visualize the results of these algorithms in terms of client power consumption and response time. Besides being able to step through an algorithms execution as it considers various operator placement decisions, attendees are able to experiment with different scenarios by customizing the parameters of the query workloads (e.g., changing the selectivities and projectivities of the operators) or the clients profile (e.g., power consumed per unit of time of processing) and examine the impact.