4,974 research outputs found
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems
(CPS) present novel challenges to Big Data platforms for performing online
analytics. Ubiquitous sensors from IoT deployments are able to generate data
streams at high velocity, that include information from a variety of domains,
and accumulate to large volumes on disk. Complex Event Processing (CEP) is
recognized as an important real-time computing paradigm for analyzing
continuous data streams. However, existing work on CEP is largely limited to
relational query processing, exposing two distinctive gaps for query
specification and execution: (1) infusing the relational query model with
higher level knowledge semantics, and (2) seamless query evaluation across
temporal spaces that span past, present and future events. These allow
accessible analytics over data streams having properties from different
disciplines, and help span the velocity (real-time) and volume (persistent)
dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP)
framework that provides domain-aware knowledge query constructs along with
temporal operators that allow end-to-end queries to span across real-time and
persistent streams. We translate this query model to efficient query execution
over online and offline data streams, proposing several optimizations to
mitigate the overheads introduced by evaluating semantic predicates and in
accessing high-volume historic data streams. The proposed X-CEP query model and
execution approaches are implemented in our prototype semantic CEP engine,
SCEPter. We validate our query model using domain-aware CEP queries from a
real-world Smart Power Grid application, and experimentally analyze the
benefits of our optimizations for executing these queries, using event streams
from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems,
October 27, 201
Database Queries that Explain their Work
Provenance for database queries or scientific workflows is often motivated as
providing explanation, increasing understanding of the underlying data sources
and processes used to compute the query, and reproducibility, the capability to
recompute the results on different inputs, possibly specialized to a part of
the output. Many provenance systems claim to provide such capabilities;
however, most lack formal definitions or guarantees of these properties, while
others provide formal guarantees only for relatively limited classes of
changes. Building on recent work on provenance traces and slicing for
functional programming languages, we introduce a detailed tracing model of
provenance for multiset-valued Nested Relational Calculus, define trace slicing
algorithms that extract subtraces needed to explain or recompute specific parts
of the output, and define query slicing and differencing techniques that
support explanation. We state and prove correctness properties for these
techniques and present a proof-of-concept implementation in Haskell.Comment: PPDP 201
Formal Methods for Constraint-Based Testing and Reversible Debugging in Erlang
Tesis por compendio[ES] Erlang es un lenguaje de programación funcional con concurrencia mediante paso de mensajes basado en el modelo de actores. Éstas y otras características lo hacen especialmente adecuado para aplicaciones distribuidas en tiempo real acrítico. En los últimos años, la popularidad de Erlang ha aumentado debido a la demanda de servicios concurrentes.
No obstante, desarrollar sistemas Erlang libres de errores es un reto considerable. A pesar de que Erlang evita muchos problemas por diseño (por ejemplo, puntos muertos), algunos otros problemas pueden aparecer. En este contexto, las técnicas de testing y depuración basadas en métodos formales pueden ser útiles para detectar, localizar y arreglar errores de programación en Erlang.
En esta tesis proponemos varios métodos para testing y depuración en Erlang. En particular, estos métodos están basados en modelos semánticos para concolic testing, pruebas basadas en propiedades, depuración reversible con consistencia causal y repetición reversible con consistencia causal de programas Erlang. Además, probamos formalmente las principales propiedades de nuestras propuestas y diseñamos herramientas de código abierto que implementan estos métodos.[CA] Erlang és un llenguatge de programació funcional amb concurrència mitjançant pas de missatges basat en el model d'actors. Estes i altres característiques el fan especialment adequat per a aplicacions distribuïdes en temps real acrític. En els últims anys, la popularitat d'Erlang ha augmentat degut a la demanda de servicis concurrents.
No obstant, desenvolupar sistemes Erlang lliures d'errors és un repte considerable. Encara que Erlang evita molts problemes per disseny (per exemple, punts morts), alguns altres problemes poden aparéixer. En este context, les tècniques de testing y depuració basades en mètodes formals poden ser útils per a detectar, localitzar y arreglar errors de programació en Erlang.
En esta tesis proposem diversos mètodes per a testing i depuració en Erlang. En particular, estos mètodes estan basats en models semàntics per a concolic testing, testing basat en propietats, depuració reversible amb consistència causal i repetició reversible amb consistència causal de programes Erlang. A més, provem formalment les principals propietats de les nostres propostes i dissenyem ferramentes de codi obert que implementen estos mètodes.[EN] Erlang is a message-passing concurrent, functional programming language based on the actor model. These and other features make it especially appropriate for distributed, soft real-time applications. In the recent years, Erlang's popularity has increased due to the demand for concurrent services.
However, developing error-free systems in Erlang is quite a challenge. Although Erlang avoids many problems by design (e.g., deadlocks), some other problems may appear. Here, testing and debugging techniques based on formal methods may be helpful to detect, locate and fix programming errors in Erlang.
In this thesis we propose several methods for testing and debugging in Erlang. In particular, these methods are based on semantics models for concolic testing, property-based testing, causal-consistent reversible debugging and causal-consistent replay debugging of Erlang programs. We formally prove the main properties of our proposals and design open-source tools that implement these methods.Palacios Corella, A. (2020). Formal Methods for Constraint-Based Testing and Reversible Debugging in Erlang [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/139076TESISCompendi
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks
This paper investigates the use of deep reinforcement learning (DRL) in a MAC
protocol for heterogeneous wireless networking referred to as
Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is
partially inspired by the vision of DARPA SC2, a 3-year competition whereby
competitors are to come up with a clean-slate design that "best share spectrum
with any network(s), in any environment, without prior knowledge, leveraging on
machine-learning technique". Specifically, this paper considers the problem of
sharing time slots among a multiple of time-slotted networks that adopt
different MAC protocols. One of the MAC protocols is DLMA. The other two are
TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC
protocols are TDMA and ALOHA. Yet, by a series of observations of the
environment, its own actions, and the resulting rewards, a DLMA node can learn
an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes
according to a specified objective (e.g., the objective could be the sum
throughput of all networks, or a general alpha-fairness objective)
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