Theoretical analysis of the SABUL congestion control algorithm

Several new protocols such as RBUDP, User-Level UDP, Tsunami, and SABUL, have been proposed as alternatives to TCP for high speed data transfer. The purpose of this paper is to analyze the effects of SABUL congestion control algorithm on SABUL performance metrics such as bandwidth utilization, self-fairness, aggressiveness and average packet losses. We propose simple deterministic and stochastic models of SABUL congestion control algorithm and use the models to assess these metrics. Our results explain SABUL throughput oscillations, derive bounds on its aggressiveness/responsiveness, show that SABUL can be self-fair, and identify conditions under which SABUL connections may experience excessive packet losses. This work was sponsored in part by the National Science Foundation grant No. NSF-9901004, DOE SciDAC grant DE-FC02-01ER25484, and IBM Corp. Shared University Research Program.     

The TSIMMIS Approach to Mediation: Data Models and Languages

TSIMMIS—The Stanford-IBM Manager of Multiple InformationSources—is a system for integrating information. It offers a datamodel and a common query language that are designed to support thecombining of information from many different sources. It also offerstools for generating automatically the components that are needed tobuild systems for integrating information. In this paper we shalldiscuss the principal architectural features and their rationale.     

Generalized Hirsch h-index for disclosing latent facts in citation networks

What is the value of a scientist and its impact upon the scientific thinking? How can we measure the prestige of a journal or a conference? The evaluation of the scientific work of a scientist and the estimation of the quality of a journal or conference has long attracted significant interest, due to the benefits by obtaining an unbiased and fair criterion. Although it appears to be simple, defining a quality metric is not an easy task. To overcome the disadvantages of the present metrics used for ranking scientists and journals, J. E. Hirsch proposed a pioneering metric, the now famous h-index. In this article we demonstrate several inefficiencies of this index and develop a pair of generalizations and effective variants of it to deal with scientist ranking and publication forum ranking. The new citation indices are able to disclose trendsetters in scientific research, as well as researchers that constantly shape their field with their influential work, no matter how old they are. We exhibit the effectiveness and the benefits of the new indices to unfold the full potential of the h-index, with extensive experimental results obtained from the DBLP, a widely known on-line digital library.     

Supporting inconsistent rules in database systems

When a set of rules generates (conflicting) values for a virtual attribute of some tuple, the system must resolve the inconsistency and decide on a unique value that is assigned to that attribute. In most current systems, the conflict is resolved based on criteria that choose one of the rules in the conflicting set and use the value that it generated. There are several applications, however, where inconsistencies of the above form arise, whose semantics demand a different form of resolution. We propose a general framework for the study of the conflict resolution problem, and suggest a variety of resolution criteria, which collectively subsume all previously known solutions. With several new criteria being introduced, the semantics of several applications are captured more accurately than in the past. We discuss how conflict resolution criteria can be specified at the schema or the rule-module level. Finally, we suggest some implementation techniques based on rule indexing, which allow conflicts to be resolved efficiently at compile time, so that at run time only a single rule is processed.An earlier version of this work appeared under the title Conflict Resolution of Rules Assigning Values to Virtual Attributes inProceedings of the 1989 ACM-Sigmod Conference, Portland, OR, June 1989, pp. 205–214.Partially supported by the National Science Foundation under Grant IRI-9157368 (PYI Award) and by grants from DEC, HP, and AT&T.Partially supported by the National Science Foundation under Grant IRI-9057573 (PYI Award), IBM, DEC, and the University of Maryland Institute for Advanced Computer Studies (UMIACS).     

New plane-sweep algorithms for distance-based join queries in spatial databases

Efficient and effective processing of the distance-based join query (DJQ) is of great importance in spatial databases due to the wide area of applications that may address such queries (mapping, urban planning, transportation planning, resource management, etc.). The most representative and studied DJQs are the K Closest Pairs Query (KCPQ) and εDistance Join Query (εDJQ). These spatial queries involve two spatial data sets and a distance function to measure the degree of closeness, along with a given number of pairs in the final result (K) or a distance threshold (ε). In this paper, we propose four new plane-sweep-based algorithms for KCPQs and their extensions for εDJQs in the context of spatial databases, without the use of an index for any of the two disk-resident data sets (since, building and using indexes is not always in favor of processing performance). They employ a combination of plane-sweep algorithms and space partitioning techniques to join the data sets. Finally, we present results of an extensive experimental study, that compares the efficiency and effectiveness of the proposed algorithms for KCPQs and εDJQs. This performance study, conducted on medium and big spatial data sets (real and synthetic) validates that the proposed plane-sweep-based algorithms are very promising in terms of both efficient and effective measures, when neither inputs are indexed. Moreover, the best of the new algorithms is experimentally compared to the best algorithm that is based on the R-tree (a widely accepted access method), for KCPQs and εDJQs, using the same data sets. This comparison shows that the new algorithms outperform R-tree based algorithms, in most cases.     

The contribution of proteomics towards deciphering the enigma of Coxiella burnetii

Coxiella burnetii, the causative agent of Q fever, is an intracellular bacterium and a potential weapon for bioterrorism. The widespread throughout the world, zoonosis is manifested clinically as a self-limited febrile illness, as pneumonia (acute Q fever) or as a chronic illness with endocarditis being its major complication. The recent Netherlands Q fever outbreak has driven the bacterium from a relatively cryptic, underappreciated, “niche” microorganism on the sideline of bacteriology, to one of possibly great impact on public health. Advances in the study of this microorganism proceeded slowly, primarily due to the, until recently, obligatory intracellular nature of the pathogen that in its virulent phase I must be manipulated under biosafety level-3 conditions. Proteomic studies, in particular, have generated a vast amount of information concerning several aspects of the bacterium such as virulence factors, detection/diagnostic and immunogenic biomarkers, inter-/intraspecies variation, resistance to antibiotics, and secreted effector proteins with significant clinical impact. The phenomenon observed following the genomics era, that of generation and accumulation of huge amount of data that ultimately end up unexploited on several databases, begins to emerge in the proteomics field as well. This review will focus on the advances in the field of C. burnetii proteomics through MS, attempting in parallel to utilize some of the proteomics findings by suggesting future directions for the improvement of Q fever diagnosis and therapy.     

A New Neuro-FDS Definition for Indirect Adaptive Control of Unknown Nonlinear Systems Using a Method of Parameter Hopping

The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an FDS and then the fuzzy rules are approximated by appropriate HONNFs. Thus, the identification scheme leads up to a recurrent high-order neural network (RHONN), which however takes into account the fuzzy output partitions of the initial FDS. The proposed scheme does not require a priori experts' information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions. Once the system is identified around an operation point, it is regulated to zero adaptively. Weight updating laws for the involved HONNFs are provided, which guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. The existence of the control signal is always assured by introducing a novel method of parameter hopping, which is incorporated in the weight updating law. Simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given. Also, the applicability of the method is tested on a direct current (dc) motor system where it is shown that by following the proposed procedure one can obtain asymptotic regulation.