1,490 research outputs found
Integrating heterogeneous distributed COTS discrete-event simulation packages: An emerging standards-based approach
This paper reports on the progress made toward the emergence of standards to support the integration of heterogeneous discrete-event simulations (DESs) created in specialist support tools called commercial-off-the-shelf (COTS) discrete-event simulation packages (CSPs). The general standard for heterogeneous integration in this area has been developed from research in distributed simulation and is the IEEE 1516 standard The High Level Architecture (HLA). However, the specific needs of heterogeneous CSP integration require that the HLA is augmented by additional complementary standards. These are the suite of CSP interoperability (CSPI) standards being developed under the Simulation Interoperability Standards Organization (SISO-http://www.sisostds.org) by the CSPI Product Development Group (CSPI-PDG). The suite consists of several interoperability reference models (IRMs) that outline different integration needs of CSPI, interoperability frameworks (IFs) that define the HLA-based solution to each IRM, appropriate data exchange representations to specify the data exchanged in an IF, and benchmarks termed CSP emulators (CSPEs). This paper contributes to the development of the Type I IF that is intended to represent the HLA-based solution to the problem outlined by the Type I IRM (asynchronous entity passing) by developing the entity transfer specification (ETS) data exchange representation. The use of the ETS in an illustrative case study implemented using a prototype CSPE is shown. This case study also allows us to highlight the importance of event granularity and lookahead in the performance and development of the Type I IF, and to discuss possible methods to automate the capture of appropriate values of lookahead
Programming in logic without logic programming
In previous work, we proposed a logic-based framework in which computation is
the execution of actions in an attempt to make reactive rules of the form if
antecedent then consequent true in a canonical model of a logic program
determined by an initial state, sequence of events, and the resulting sequence
of subsequent states. In this model-theoretic semantics, reactive rules are the
driving force, and logic programs play only a supporting role.
In the canonical model, states, actions and other events are represented with
timestamps. But in the operational semantics, for the sake of efficiency,
timestamps are omitted and only the current state is maintained. State
transitions are performed reactively by executing actions to make the
consequents of rules true whenever the antecedents become true. This
operational semantics is sound, but incomplete. It cannot make reactive rules
true by preventing their antecedents from becoming true, or by proactively
making their consequents true before their antecedents become true.
In this paper, we characterize the notion of reactive model, and prove that
the operational semantics can generate all and only such models. In order to
focus on the main issues, we omit the logic programming component of the
framework.Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
Keeping Authorities "Honest or Bust" with Decentralized Witness Cosigning
The secret keys of critical network authorities - such as time, name,
certificate, and software update services - represent high-value targets for
hackers, criminals, and spy agencies wishing to use these keys secretly to
compromise other hosts. To protect authorities and their clients proactively
from undetected exploits and misuse, we introduce CoSi, a scalable witness
cosigning protocol ensuring that every authoritative statement is validated and
publicly logged by a diverse group of witnesses before any client will accept
it. A statement S collectively signed by W witnesses assures clients that S has
been seen, and not immediately found erroneous, by those W observers. Even if S
is compromised in a fashion not readily detectable by the witnesses, CoSi still
guarantees S's exposure to public scrutiny, forcing secrecy-minded attackers to
risk that the compromise will soon be detected by one of the W witnesses.
Because clients can verify collective signatures efficiently without
communication, CoSi protects clients' privacy, and offers the first
transparency mechanism effective against persistent man-in-the-middle attackers
who control a victim's Internet access, the authority's secret key, and several
witnesses' secret keys. CoSi builds on existing cryptographic multisignature
methods, scaling them to support thousands of witnesses via signature
aggregation over efficient communication trees. A working prototype
demonstrates CoSi in the context of timestamping and logging authorities,
enabling groups of over 8,000 distributed witnesses to cosign authoritative
statements in under two seconds.Comment: 20 pages, 7 figure
Modeling the Internet of Things: a simulation perspective
This paper deals with the problem of properly simulating the Internet of
Things (IoT). Simulating an IoT allows evaluating strategies that can be
employed to deploy smart services over different kinds of territories. However,
the heterogeneity of scenarios seriously complicates this task. This imposes
the use of sophisticated modeling and simulation techniques. We discuss novel
approaches for the provision of scalable simulation scenarios, that enable the
real-time execution of massively populated IoT environments. Attention is given
to novel hybrid and multi-level simulation techniques that, when combined with
agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches,
can provide means to perform highly detailed simulations on demand. To support
this claim, we detail a use case concerned with the simulation of vehicular
transportation systems.Comment: Proceedings of the IEEE 2017 International Conference on High
Performance Computing and Simulation (HPCS 2017
TEMPOS: A Platform for Developing Temporal Applications on Top of Object DBMS
This paper presents TEMPOS: a set of models and languages supporting the manipulation of temporal data on top of object DBMS. The proposed models exploit object-oriented technology to meet some important, yet traditionally neglected design criteria related to legacy code migration and representation independence. Two complementary ways for accessing temporal data are offered: a query language and a visual browser. The query language, namely TempOQL, is an extension of OQL supporting the manipulation of histories regardless of their representations, through fully composable functional operators. The visual browser offers operators that facilitate several time-related interactive navigation tasks, such as studying a snapshot of a collection of objects at a given instant, or detecting and examining changes within temporal attributes and relationships. TEMPOS models and languages have been formalized both at the syntactical and the semantical level and have been implemented on top of an object DBMS. The suitability of the proposals with regard to applications' requirements has been validated through concrete case studies
Introducing polyglot-based data-flow awareness to time-series data stores
The rising interest in extracting value from data has led to a broad proliferation of monitoring infrastructures, most notably composed by sensors, intended to collect this new oil. Thus, gathering data has become fundamental for a great number of applications, such as predictive maintenance techniques or anomaly detection algorithms. However, before data can be refined into insights and knowledge, it has to be efficiently stored and prepared for its later retrieval. As a consequence of this sensor and IoT boom, Time-Series databases (TSDB), designed to manage sensor data, became the fastest-growing database category since 2019. Here we propose a holistic approach intended to improve TSDB’s performance and efficiency. More precisely, we introduce and evaluate a novel polyglot-based approximation, aimed to tailor the data store, not only to time-series data –as it is done conventionally– but also to the data flow itself: From its ingestion, until its retrieval. In order to evaluate the approach, we materialize it in an alternative implementation of NagareDB, a resource-efficient time-series database, based on MongoDB, in turn, the most popular NoSQL storage solution. After implementing our approach into the database, we observe a global speed up, solving queries up to 12 times faster than MongoDB’s recently launched Time-series capability, as well as generally outperforming InfluxDB, the most popular time-series database. Our polyglot-based data-flow aware solution can ingest data more than two times faster than MongoDB, InfluxDB, and NagareDB’s original implementation, while using the same disk space as InfluxDB, and half of the requested by MongoDB.This research was partly supported by the Spanish Ministry of Science and Innovation (contract PID2019-107255GB) and by the Generalitat de Catalunya (contract 2017-SGR-1414).Peer ReviewedPostprint (published version
Data mining by means of generalized patterns
The thesis is mainly focused on the study and the application of pattern discovery algorithms that aggregate database knowledge to discover and exploit valuable correlations, hidden in the analyzed data, at different abstraction levels. The aim of the research effort described in this work is two-fold: the discovery of associations, in the form of generalized patterns, from large data collections and the inference of semantic models, i.e., taxonomies and ontologies, suitable for driving the mining proces
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