waashk/dblp huggingface dataset:
convrel: relationship conversion to xml nested structures. this paper addresses the problem of efficiently transforming relational schemas into nested-based xml schemas. the proposed conversion algorithm considers preserving the structural constraints, cardinality and participation ratios, by analyzing each relation in relationship to others. several candidate xml structures are proposed for each type of relationship according to its structural constraints from which the optimum one is chosen in terms of compact nested xml structure and determining the smallest xml data file
a "go with the winners" approach to finding frequent patterns. in their seminal work on go with the winners (gww) algorithms, d. aldous and u. vazirani [3] proved a sufficient condition for the number of particles needed for reaching the bottom of a tree with high probability via a gww random walk. however, to use this result in practice would require knowledge of the entire search tree which is infeasible for most problems. in this paper we improve slightly on this situation by deriving a recurrence relation that provides an upper-bound for a tree's imbalance in terms of the imbalance between tree levels that are close to one another, provided that these latter imbalances can be measured with sufficient accuracy.we then turn our attention to the problem of finding both frequent and infrequent patterns in a database. one of the most widely used algorithms for finding frequent patterns in memory-resident databases is a randomized algorithm first proposed by gunopulos et al. [12]. we show that such an algorithm is precisely one for which the gww paradigm was designed to improve on. experimental results using the splice-junction gene sequences database [4] are also provided and lend empirical evidence of the benefits of using gww.
genetic programming for data classification: partitioning the search space. when genetic programming is used to evolve decision trees for data classification, search spaces tend to become extremely large. we present several methods using techniques from the field of machine learning to refine and thereby reduce the search space sizes for decision tree evolvers. we will show that these refinement methods improve the classification performance of our algorithms.
authentication and access delegation with user-released certificates. we propose an authentication and access delegation system based on an unconventional use of x.509 certificates. it allows users to connect from any untrusted machine and to define dynamically a group of trusted co-workers. it is low cost, doesn't need unusual software nor hardware on the client's side, and offers a good degree of security without requiring that the user be too careful. the underlying idea is to enable users to release their own certificates with very short life span (or usable just once) to authenticate themselves to the server.
anonymity and certification: e-mail, a case study. awareness of legal constraints regarding the use and provision of electronic systems, leads us to question the feasibility and applicability of technical solutions that take into account security and privacy regulations. we discuss the issue with reference to directives of the european community and italian legislation. in particular we study the case of e-mail, proposing a protocol that retains as many characteristics of the e-mail as possible, yet allowing for complete anonymity and proofs of correct deploy. we discuss the implications of taking anonymity to the extremes and evaluate the limits of the protocol.
a flexible approach to user-defined symbolic granularities in temporal databases. user-defined granularities, calendars and periodicities are gaining an increasing relevance in the area of temporal databases (tdb). in this paper, we discuss the advantages of adopting for tdbs a modular framework, based on a family of symbolic languages, defined starting from a set of mutually orthogonal properties that characterize periodicity. first, we show that one of the languages covers the notion of granularity (as defined in the tdb glossary), so that it can be used in order to define validity times of facts in a tdb. second, we discuss the usefulness of using more expressive languages in the family to achieve such a goal. third, we explore how such languages can be used in order to deal in an intensional way with infinite periodic data in temporal (relational) databases.
ontology-focused crawling of web documents. the web, the largest unstructured database of the world, has greatly improved access to documents. however, documents on the web are largely disorganized. due to the distributed nature of the world wide web it is difficult to use it as a tool for information and knowledge management. therefore, users doing the difficult task of exploring the web have to be supported by intelligent means.this paper proposes an approach for document discovery building on a comprehensive framework for ontology-focused crawling of web documents. our framework includes means for using a complex ontology and associated instance elements. it defines several relevance computation strategies and provides an empirical evaluation which has shown promising results.
behavioral pattern analysis: towards a new representation of systems requirements based on actions and events. requirements are descriptions of the application domain, of the problems to be solved there, and of the system(s) to be built in that domain to solve these problems. many projects have failed because their requirements were inadequately analyzed or described. use cases have offered analysts and developers an approach for capturing the end-user requirements in an object-oriented fashion. however, experience is showing some problems with use cases such as lack of a precise definition that led most companies to re-invent their own version, lack of notion of atomicity, and lack of addressing all interaction types. this research is concerned with developing a more effective alternative to use case analysis (uca) for modeling the functional requirements of human-machine safety-critical real-time systems. the new alternative modeling approach, behavioral pattern analysis (bpa), is an event-oriented approach in which events are considered the primary entities of the world domain requirements model. while the term 'event' is used in uml, and in almost all of the other modeling approaches, to mean an occurrence of stimulus that can trigger a state transition, the event described in bpa is a real-life conceptual entity that is unrelated to any implementation. it describes what, who, how, when, where, and why for an interaction or set of interactions between entities. the appeal to events is natural in the definition of requirements because events provide more complete and clearer understanding of the defined requirements. such completeness and clarity are keys to the effectiveness of requirements modeling. it is the researcher's belief that the system model should be event driven rather than use case driven. three real-life applications were used to validate the effectiveness of the new bpa approach in modeling the functional requirements of safety-critical real-time systems. both, the uca and the bpa approaches were used to model the functional requirements of these applications. to validate the bpa approach improved effectiveness, the first application was used in a pilot case study by the researcher to provide a proof of concept, and the other two applications were used in another sixteen case studies that were arranged for the evaluation purpose. in all of these case studies, the resulting models from both approaches were compared using safety, cmm repeatability, and the ansi/ieee std 830-1984 effectiveness standards as the basis of the comparison. the case studies result indicated that at least eighty seven percent (87%) of the participating subject matter experts (smes) believe that bpa is more effective than uca in the definition of requirements of safety-critical and real-time systems. (abstract shortened by umi.)
application of fuzzy logic to multiple criteria decision making in aquacultural planning. the field of regional planning is characterized by the large number of issues and attributes involved, and regional planning for aquaculture development is no exception. moreover, aquacultural plans do not have clearly defined objectives and require information that, if exist, is often imprecise and uncertain.this paper applies fuzzy set theory to multiple criteria decision making (mcdm) in aquaculture planning. in effect, the paper demonstrates how fuzzy set theory can be used to explicitly account for the inherent uncertainty encountered when planning for aquaculture development in a given region. a case study for regional aquaculture planning in northern egypt demonstrates the proposed fuzzy mcdm framework.
dynamic consistency checking for temporal and spatial relations in multimedia presentations. creating complex multimedia presentations involves the specification of temporal and spatial relations in the form of constraints. however, some of these constraints could contradict each other and hence lead to an inconsistency. the user may not be aware of this inconsistency while authoring. hence this inconsistency has to be identified and removed by the presentation process prior to the play-out. in this paper, we examined an existing work based on graph theory for consistency checking. we propose a modification to this approach which simplifies the algorithm, reduces its total running time, and helps to make it dynamic. another salient feature of our paper is the introduction of new temporal and spatial operators with higher expressive power than traditional ones. thus, this paper presents a multimedia presentation mechanism, which dynamically maintains a consistent and complete set of constraints during authoring and play-out of the presentation.
a hybrid approach for multiresolution modeling of large-scale scientific data. simulations of complex scientific phenomena involve the execution of massively parallel computer programs. these simulation programs generate large-scale multidimensional data sets over the spatio-temporal region. analyzing such massive data sets is an essential step in helping scientists glean new information. to this end, efficient and effective data models are needed. in this paper, we present a hybrid approach for constructing data models from large-scale multidimensional scientific data sets. our models not only provide descriptive information about the data but also allow users to subsequently examine the data by querying the data models. our approach combines a multiresolution-topological model of the data with a multivariate-physical model of the data to generate one hierarchical data model that efficiently captures both the spatio-temporal and the physical aspects of the data. in particular, this hybrid approach consists of three phases. in the first phase, we build a multiresolution model that encapsulates the data set's spatial information (i.e., topology and spatial connectivity). in the second phase, we build a multivariate model from the physical dimensions of the data set. physical dimensions refer to those dimensions that are neither spatial (x, y, z) nor temporal (time). the exclusion of the spatial-temporal dimensions from the clustering phase is important since "similar" characteristics could be located (spatially) far from each other. finally, in the third phase, we connect the multivariate-physical model to the multiresolution-topological model by utilizing ideas from information retrieval. the third phase is essential since the multivariate-physical model does not contain any topological information (without which the model does not have accurate spatial context information). experimental evaluations on two large-scale multidimensional scientific data sets illustrate the value of our hybrid approach.
evaluating collaborative software in supporting organizational learning with bayesian networks. many collaborative software tools have been developed in the recent years to accelerate the growing interest of many organizations to become learning organizations. selecting a collaborative tool that best suits an organization's needs is a challenging task, given that there are no evaluation criteria against which these tools could be evaluated with respect to various organizational learning concepts. the objective of this paper is twofold. first, it derives a generic set of criteria required to evaluate the suitability of a given collaborative tool in supporting the mental model concept of organizational learning. second, it investigates the possibility of using bayesian networks as an evaluation methodology to rate the suitability of a given collaborative tool with respect to how well it meets the derived evaluation criteria.
infofilter: a system for expressive pattern specification and detection over text streams. information filtering includes monitoring text streams to detect patterns that are more complex than those handled by search engines. text stream monitoring and pattern detection have far reaching applications such as tracking information flow among terrorist outfits, web parental control, and business intelligence. pattern characterization requirements of applications entail an expressive language for specifying patterns than what is currently provided by information retrieval query languages (irqls) and current information filtering systems. pattern specification alone does not suffice, as detecting these complex patterns is equally important in order to use these systems for real-world applications.infofilter, a content-based information filtering system, presented in this paper, allows users to specify complex patterns and detects these patterns in incoming text streams from various sources such as news feed, emails, web pages and caption text from streaming videos. complex patterns such as combinations of sequential, structural patterns, wild cards, word frequencies, proximity, boolean operators and synonyms are formulated using the expressive pattern specification language, psl, proposed in this paper. once specified, these complex patterns are detected using a data flow paradigm over pattern detection graphs (pdgs).
compiling regular patterns to sequential machines. pattern matching combined with regular expressions has many applications including semistructured data matching and lexical analysis in compilers. variables in patterns allow one to refer to parts of the matching input. but some regular patterns suffer from inherent ambiguity, yielding more than one valid result. a match policy like shortest or longest match can disambiguate such patterns.in this paper, we show that regular pattern matching corresponds to sequential transduction. we derive straightforward ways to optimally compile regular patterns to sequential machines and to decide when regular patterns are unambiguous. unambiguous patterns can be matched in a single traversal of the input. ambiguities in patterns correspond to nondeterminism in sequential machines. applying the match policy optimally yields two deterministic sequential machines, which produce the shortest match in two consecutive runs.
code generation techniques for developing light-weight xml web services for embedded devices. this paper presents specialized code generation techniques and runtime optimizations for developing light-weight xml web services for embedded devices. the optimizations are implemented in the gsoap web services development environment for c and c++. the system supports the industry-standard xml-based web services protocols that are intended to deliver universal access to any networked application that supports xml. with the standardization of the web services protocols and the availability of toolkits such as gsoap for developing embedded web services, new opportunities emerge to integrate embedded systems into larger frameworks of interconnected applications and systems accessing dynamic resources on the web ranging from hand-held and embedded devices to databases, clusters, and grids.
editorial message: special track on distributed systems and grid computing. it is a great pleasure to welcome you to the 21st annual acm symposium on applied computing, special track on distributed systems and grid computing (dsgc) in dijon, france. the objective of the dsgc track is to provide a forum for scientists, engineers and practitioners in academia, industry and research institutes to share technical ideas, experiences and results and to present their latest findings in any aspects of parallel, distributed, and grid computing. the topics of the track emphasize the design, architecture, and software of distributed systems and grid computing environments with their scientific and engineering applications.
validation of code-improving transformations for embedded systems. programmers of embeded systems often develop software in assembly code due to inadequate support from compilers and the need to meet critical speed and/or space constraints. many embeded applications are being used as a component of an increasing number of critical systems. while achieving high performance for these systems is important, ensuring that these systems execute correctly is vital. one portion of this process is to ensure that code-improving transformations applied to a program will not change the program's semantic behavior, which may be jeopardized when transformations are specified manually. this paper describes a general approach for validation of many low-level code-improving transformations made either by a compiler or specified by hand. initially, we associate a region of the program representation with a code-improving transformation. afterwards, we calculate the region's effects on the rest of the program before and after the transformation. the transformation is considered valid when the effects before and after the transformation are identical. we implemented an automatic validation system in the vpo compiler. the system is currently able to validate all code-improving transformations in vpo except transformations that affect blocks across loop levels.
wsbus: a framework for reliable web services interactions. as web services start to be deployed across enterprise boundaries and for collaborative e-business scenarios, reliable inter-application messaging and failure management becomes a critical issue to insure guaranteed and ordered delivery even in the case of system or network failures or temporary unavailability of services. this paper presents wsbus, a lightweight integration framework for dependable web services interactions. we discuss the system features and architecture as well as initial performance results.
quad and correctly rounded double precision math functions: portable and optimized for intel architectures. a fundamental part of a system's quad floating-point precision support is its companion mathematical library. we developed a hierarchical c macro based methodology for implementing the quad precision elementary functions both portable and optimized for intel&reg; architectures. when two or three floating-point values natively supported in the hardware are packed together, we are able to leverage the extra precision provided to attain high accuracy and yield measurable performance gains over traditional integer-based implementations. our high-level language codes are unified for several platforms, while native floating-point arithmetic sequences are the computational elements that underlie the macros and exploit the features of particular architecture. this significantly reduces the library maintenance cost and allows providing high performance quad functions for the new processors. we also show how language extensions in the intel&reg; c/c++ compiler allow additional performance improvements on intel&reg; architectures. finally, our experiments based on recent advances of de dinechin, defour and lauter demonstrate that using methodology developed for quad precision functions we can attain correctly rounded double precision routines with significant performance improvements compared with algorithms based on generic multi-precision packages for a low implementation cost.
scalable, efficient epidemiological simulation. we describe the design and implementation of a system for simulating the spread of disease among individuals in a large urban population over the course of several weeks. in contrast to traditional approaches, we do not assume uniform mixing among large sub-populations or split the population into spatial or demographic subpopulations determined a priori. instead, we rely on empirical estimates of the social network, or contact patterns, that are produced by transims, a large-scale simulation of transportation systems.
kala: kernel aspect language for advanced transactions. transaction management is a known cross-cutting concern. previous research has been conducted to express this concern as an aspect. however, this work uses general-purpose aspect languages which lack a formal foundation and are unable to express advanced models for transaction management. in contrast, we designed a domain-specific aspect language for advanced transaction management, called kala, that is based on a formalism for advanced transactions. as a result, kala covers the field of advanced transaction management while obtaining a much higher level of abstraction than is achieved with general-purpose aspect languages. in this paper we detail the creation process of kala.
graphical rule-based representation of signal-transduction networks. the process by which a cell senses and responds to its environment, as in signal transduction, is often mediated by a network of protein-protein interactions, in which proteins combine to form complexes and undergo post-translational modifications, which regulate their enzymatic and binding activities. a typical signaling protein contains multiple sites of protein interaction and modification and may contain catalytic domains. as a result, interactions of signaling proteins have the potential to generate a combinatorially large number of complexes and modified states, and representing signal-transduction networks can be challenging. representation, in the form of a diagram or model, usually involves a tradeoff between comprehensibility and precision: comprehensible representations tend to be ambiguous or incomplete, whereas precise representations, such as a long list of chemical species and reactions in a network, tend to be incomprehensible. here, we develop conventions for representing signal-transduction networks that are both comprehensible and precise. labeled nodes represent components of proteins and their states, and edges represent bonds between components. binding and enzymatic reactions are described by reaction rules, in which left graphs define the properties of reactants and right graphs define the products that result from transformations of reactants. the reaction rules can be evaluated to derive a mathematical model.
modeling multiple class loaders by a calculus for dynamic linking. in a recent paper we proposed a calculus for modeling dynamic linking independently of the details of a particular programming environment.here we use a particular instantiation of this calculus to encode a toy language, called mcl, which provides an abstract view of the mechanism of dynamic class loading with multiple loaders as in java.the aim is twofold. on one hand, we show an example of application of the calculus in modeling existing loading and linking policies, showing in particular that java-like loading with multiple loaders can be encoded without exploiting the full expressive power of the calculus. on the other hand, we provide a simple formal model which allows a better understanding of java-like loading mechanisms and also shows an intermediate solution between the rigid approach based only on the class path and that which allows arbitrary userdefined loaders, which can be intricate and error-prone.