Performance evaluation for single‐ and semi‐layer multistage interconnection networks servicing multicast traffic by full multicast operation

Here a novel analytical method for performance prediction estimation of single‐ and multi‐layer multistage interconnection networks (MINs) under multicast environments is presented. The ‘Cell Replication While Routing’ is used as a packet routing technique and the ‘full multicast’ mode as transmission policy is employed in all the MINs under study. This model is developed for single‐layer MINs and is extended and applied to special types of multi‐layer MINs, called semi‐layer MINs, which support mixed traffic patterns such as unicast and multicast in uniform traffic conditions. The results confirm the significant performance saturation of single‐layer MINs and the improvement of corresponding performance indicators by semi‐layer MINs. The proposed analytical framework is anticipated to be a very useful tool in evaluating the performance of semi‐layer MINs in designing more efficient QoS networks. Copyright © 2010 John Wiley & Sons, Ltd.     

An adaptive RFID middleware for supporting metaphysical data independence

Sensor devices produce data that are unreliable, low-level, and seldom able to be used directly by applications. In this paper, we propose metaphysical data independence (MDI), a layer of independence that shields applications from the challenges that arise when interacting directly with sensor devices. The key philosophy behind MDI is that applications do not deal with any aspect of physical device data, but rather interface with a high-level reconstruction of the physical world created by a sensor infrastructure. As a concrete instantiation of MDI in such a sensor infrastructure, we detail MDI-SMURF, a Radio Frequency Identification (RFID) middleware system that alleviates issues associated with using RFID data through adaptive techniques based on a novel statistical framework. Met·a·phys·ics: A division of philosophy that is concerned with the fundamental nature of reality and being [2].     

Using RSS feeds for effective mobile web browsing

While mobile phones are becoming more popular, wireless communication vendors and device manufacturers are seeking new applications for their products. Access to the large corpus of Internet information is a very prominent field. However, the technical limitations of mobile devices pose many challenges. Browsing the Internet using a mobile phone is a large scientific and cultural challenge. Web content must be adapted before it can be accessed by a mobile browser. This work presents a new methodology that uses Really Simple Syndication (RSS) feeds for the adaptation of web content for use in mobile phones. This methodology is based on concrete design guidelines and supports different viewing modes. The mobile tool provided using the RSS feeds is evaluated based on user-centered evaluation and the results are presented.     

Ground temperature estimations using simplified analytical and semi-empirical approaches

In this work, subsurface ground temperature profiles are estimated by exploiting two different approaches. In the first one, an analytical model is examined which, considering a quasi steady state system, implements the superposition of annual and daily sinusoidal fluctuations. In the second one, semi-empirical models are developed based on the general formula of the preceding, by replacing the steady state soil temperature with easily obtained daily average temperatures. Various subsets of soil temperature were used for model development, in order to explore the possibility of minimizing data requirements. Comparison of observational data with model results reveals that the observational patterns of hourly soil temperature are fairly well approximated by both by the analytical and the semi-empirical models. All models seem to capture the main characteristics of the annual course of soil temperature, with the results obtained from the semi-empirical models fluctuating in a much more realistic way than those of the analytical model. It is concluded that the proposed models may serve as useful tools for estimating and predicting soil temperatures to be used as practical reference in various environmental and energy applications.     

Topology discovery in heterogeneous IP networks: the NetInventory system

Knowledge of the up-to-date physical topology of an IP network is crucial to a number of critical network management tasks, including reactive and proactive resource management, event correlation, and root-cause analysis. Given the dynamic nature of today's IP networks, keeping track of topology information manually is a daunting (if not impossible) task. Thus, effective algorithms for automatically discovering physical network topology are necessary. Earlier work has typically concentrated on either 1) discovering logical (i.e., layer-3) topology, which implies that the connectivity of all layer-2 elements (e.g., switches and bridges) is ignored, or 2) proprietary solutions targeting specific product families. In this paper, we present novel algorithms for discovering physical topology in heterogeneous (i.e., multi-vendor) IP networks. Our algorithms rely on standard SNMP MIB information that is widely supported by modern IP network elements and require no modifications to the operating system software running on elements or hosts. We have implemented the algorithms presented in this paper in the context of the NetInventory topology-discovery tool that has been tested on Lucent's own research network. The experimental results clearly validate our approach, demonstrating that our tool can consistently discover the accurate physical network topology with reasonably small running-time requirements even for fairly large network configurations.     

Tumor-targeted polydiacetylene micelles for in vivo imaging and drug delivery

In vivo tumor targeting and drug delivery properties of small polymerized polydiacetylene (PDA) micelles (～10 nm) is investigated in a murine MDA-MB-231 xenograft model of breast cancer. Three micelles with different surface coatings are synthesized and tested for their ability to passively target tumor through the enhanced permeability and retention effect. After injection (24 h), fluorescence diffuse optical tomographic imaging indicates a tumor uptake of nearly 3% of the injected dose for the micelles with a 2 kDa poly(ethylene glycol) (PEG)-coating (PDA-PEG2000). The uptake of PDA micelles in tumors is confirmed by co-localization with [(18) F]-fluorodeoxyglucose (FDG) positron emission tomography. Although FDG has a higher diffusion rate in tumors, 40 ± 19% of the retained micelles is co-registered with the tumor volume visualized by FDG. Finally, PDA-PEG2000 micelles are loaded with the hydrophobic anticancer drug paclitaxel and used in vivo to inhibit tumor growth. These findings demonstrate the potential of PDA-PEG2000 micelles for both in vivo tumor imaging and drug delivery applications.     

Scalable filtering of XML data for Web services

As the Web gains prevalence as an application-to-application communication medium, organizations are deploying more Web service applications to provide standardized, programmatic application functionality over the Internet. The paper considers how scalable content-based routing architectures for Web applications can handle the growing number of XML messages associated with Web services.     

Mining sequential patterns with regular expression constraints

Discovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional sequential pattern mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. As a consequence, the pattern mining process is typically characterized by lack of focus and users often end up paying inordinate computational costs just to be inundated with an overwhelming number of useless results. We propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. We develop a family of novel algorithms (termed SPIRIT-Sequential Pattern mining with Regular expression consTraints) for mining frequent sequential patterns that also satisfy user-specified RE constraints. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Our solutions provide valuable insights into the trade-offs that arise when constraints that do not subscribe to nice properties (like anti monotonicity) are integrated into the mining process     

Approximate query processing using wavelets

Approximate query processing has emerged as a cost-effective approach for dealing with the huge data volumes and stringent response-time requirements of today's decision support systems (DSS). Most work in this area, however, has so far been limited in its query processing scope, typically focusing on specific forms of aggregate queries. Furthermore, conventional approaches based on sampling or histograms appear to be inherently limited when it comes to approximating the results of complex queries over high-dimensional DSS data sets. In this paper, we propose the use of multi-dimensional wavelets as an effective tool for general-purpose approximate query processing in modern, high-dimensional applications. Our approach is based on building wavelet-coefficient synopses of the data and using these synopses to provide approximate answers to queries. We develop novel query processing algorithms that operate directly on the wavelet-coefficient synopses of relational tables, allowing us to process arbitrarily complex queries entirely in the wavelet-coefficient domain. This guarantees extremely fast response times since our approximate query execution engine can do the bulk of its processing over compact sets of wavelet coefficients, essentially postponing the expansion into relational tuples until the end-result of the query. We also propose a novel wavelet decomposition algorithm that can build these synopses in an I/O-efficient manner. Finally, we conduct an extensive experimental study with synthetic as well as real-life data sets to determine the effectiveness of our wavelet-based approach compared to sampling and histograms. Our results demonstrate that our techniques: (1) provide approximate answers of better quality than either sampling or histograms; (2) offer query execution-time speedups of more than two orders of magnitude; and (3) guarantee extremely fast synopsis construction times that scale linearly with the size of the data. Received: 7 August 2000 / Accepted: 1 April 2001 Published online: 7 June 2001