Providing advanced remote medical treatment services through pervasive environments

Advances on sensor technology, wireless environments and data mining introduce new possibilities in the healthcare sector, realizing the anytime-anywhere access to medical information. Towards this direction, integration of packet-switched networks and sensor devices can be effective in deploying assistive environments, such as home monitoring for elderly or patients. In this paper we describe a policy-based architecture that utilizes wireless sensor devices, advanced network topologies and software agents to enable remote monitoring of patients and elderly people; through the aforementioned technologies we achieve continuous monitoring of a patient’s condition and we can proceed when necessary with proper actions. We also present a software framework and network architecture that realizes the provision of remote medical services, in compliance with the imposed security and privacy requirements. A proof of concept prototype is also deployed, along with an evaluation of the overall architecture’s performance.     

An analytical model for multi-epidemic information dissemination

Contemporary distributed systems usually involve the spreading of information by means of ad-hoc dialogs between nodes (peers). This paradigm resembles the spreading of a virus in the biological perspective (epidemics). Such abstraction allows us to design and implement information dissemination schemes with increased efficiency. In addition, elementary information generated at a certain node can be further processed to obtain more specific, higher-level and more valuable information. Such information carries specific semantic value that can be further interpreted and exploited throughout the network. This is also reflected in the epidemical framework through the idea of virus transmutation which is a key component in our model. We establish an analytical framework for the study of a multi-epidemical information dissemination scheme in which diverse ‘transmuted epidemics’ are spread. We validate our analytical model through simulations. Key outcomes of this study include the assessment of the efficiency of the proposed scheme and the prediction of the characteristics of the spreading process (multi-epidemical prevalence and decay).     

Perturbation Analysis and Optimization of Multiclass Multiobjective Stochastic Flow Models

Stochastic Flow Models (SFMs) are stochastic hybrid systems that abstract the dynamics of many complex discrete event systems and provide the basis for their control and optimization. SFMs have been used to date to study systems with a single user class or some multiclass settings in which performance metrics are not class-dependent. In this paper, we develop a SFM framework for multiple classes and class-dependent performance objectives, where competing classes employ threshold control policies and service is provided on a First Come First Serve (FCFS) basis. In this framework, we analyze new phenomena that result from the interaction of the different classes and give rise to a new class of “induced” events that capture delays in the SFM dynamics. We derive Infinitesimal Perturbation Analysis (IPA) estimators for derivatives of various class-dependent objectives, and use them as the basis for on-line optimization algorithms that apply to the underlying discrete event system (not the SFM). This allows us to contrast system-centric and user-centric objectives, thus putting the resource contention problem in a game framework. The unbiasedness of IPA estimators is established and numerical results are provided to illustrate the effectiveness of our method for the case where there are no constraints on the controllable thresholds and to demonstrate the gap between the results of system-centric optimization and user-centric optimization.     

Algorithm,software, and hardware optimizations for Delaunay mesh generation on simultaneous multithreaded architectures

This article focuses on the optimization of PCDM, a parallel, two-dimensional (2D) Delaunay mesh generation application, and its interaction with parallel architectures based on simultaneous multithreading (SMT) processors. We first present the step-by-step effect of a series of optimizations on performance. These optimizations improve the performance of PCDM by up to a factor of six. They target issues that very often limit the performance of scientific computing codes. We then evaluate the interaction of PCDM with a real SMT-based SMP system, using both high-level metrics, such as execution time, and low-level information from hardware performance counters.     

Efficient search based on content similarity over self-organizing P2P networks

The advent of the World Wide Web has made an enormous amount of information available to everyone and the widespread use of digital equipment enables end-users (peers) to produce their own digital content. This vast amount of information requires scalable data management systems. Peer-to-peer (P2P) systems have so far been well established in several application areas, with file-sharing being the most prominent. The next challenge that needs to be addressed is (more complex) data sharing, management and query processing, thus facilitating the delivery of a wide spectrum of novel data-centric applications to the end-user, while providing high Quality-of-Service. In this paper, we propose a self-organizing P2P system that is capable to identify peers with similar content and intentionally assign them to the same super-peer. During content retrieval, fewer super-peers need to be contacted and therefore efficient similarity search is supported, in terms of reduced network traffic and contacted peers. Our approach increases the responsiveness and reliability of a P2P system and we demonstrate the advantages of our approach using large-scale simulations.     

Two heads better than one: pattern discovery in time-evolving multi-aspect data

Data stream values are often associated with multiple aspects. For example each value observed at a given time-stamp from environmental sensors may have an associated type (e.g., temperature, humidity, etc.) as well as location. Time-stamp, type and location are the three aspects, which can be modeled using a tensor (high-order array). However, the time aspect is special, with a natural ordering, and with successive time-ticks having usually correlated values. Standard multiway analysis ignores this structure. To capture it, we propose 2 Heads Tensor Analysis (2-heads), which provides a qualitatively different treatment on time. Unlike most existing approaches that use a PCA-like summarization scheme for all aspects, 2-heads treats the time aspect carefully. 2-heads combines the power of classic multilinear analysis with wavelets, leading to a powerful mining tool. Furthermore, 2-heads has several other advantages as well: (a) it can be computed incrementally in a streaming fashion, (b) it has a provable error guarantee and, (c) it achieves significant compression ratio against competitors. Finally, we show experiments on real datasets, and we illustrate how 2-heads reveals interesting trends in the data. This is an extended abstract of an article published in the Data Mining and Knowledge Discovery journal.     

Prefetching in Content Distribution Networks via Web Communities Identification and Outsourcing

Content distribution networks (CDNs) improve scalability and reliability, by replicating content to the “edge” of the Internet. Apart from the pure networking issues of the CDNs relevant to the establishment of the infrastructure, some very crucial data management issues must be resolved to exploit the full potential of CDNs to reduce the “last mile” latencies. A very important issue is the selection of the content to be prefetched to the CDN servers. All the approaches developed so far, assume the existence of adequate content popularity statistics to drive the prefetch decisions. Such information though, is not always available, or it is extremely volatile, turning such methods problematic. To address this issue, we develop self-adaptive techniques to select the outsourced content in a CDN infrastructure, which requires no apriori knowledge of request statistics. We identify clusters of “correlated” Web pages in a site, called Web site communities, and make these communities the basic outsourcing unit. Through a detailed simulation environment, using both real and synthetic data, we show that the proposed techniques are very robust and effective in reducing the user-perceived latency, performing very close to an unfeasible, off-line policy, which has full knowledge of the content popularity.     

Computer methods for automating preoperative dental implant planning: implant positioning and size assignment

The paper presents computer-aided methods that allocate a dental implant and suggest its size, during the pre-operative planning stage, in conformance with introduced optimization criteria and established clinical requirements. Based on computed tomography data of the jaw and prosthesis anatomy, single tooth cases are planned for the best-suited implant insertion at a user-defined region. An optimum implantation axis line is produced and cylindrical implants of various candidate sizes are then automatically positioned, while their occlusal end is leveled to bone ridge, and evaluated. Radial safety margins are used for the assessment of the implant safety distance from neighboring anatomical structures and bone quantity and quality are estimated and taken into consideration. A case study demonstrates the concept and allows for its discussion.