8,918 research outputs found
Runtime Verification of Temporal Properties over Out-of-order Data Streams
We present a monitoring approach for verifying systems at runtime. Our
approach targets systems whose components communicate with the monitors over
unreliable channels, where messages can be delayed or lost. In contrast to
prior works, whose property specification languages are limited to
propositional temporal logics, our approach handles an extension of the
real-time logic MTL with freeze quantifiers for reasoning about data values. We
present its underlying theory based on a new three-valued semantics that is
well suited to soundly and completely reason online about event streams in the
presence of message delay or loss. We also evaluate our approach
experimentally. Our prototype implementation processes hundreds of events per
second in settings where messages are received out of order.Comment: long version of the CAV 2017 pape
Event Stream Processing with Multiple Threads
Current runtime verification tools seldom make use of multi-threading to
speed up the evaluation of a property on a large event trace. In this paper, we
present an extension to the BeepBeep 3 event stream engine that allows the use
of multiple threads during the evaluation of a query. Various parallelization
strategies are presented and described on simple examples. The implementation
of these strategies is then evaluated empirically on a sample of problems.
Compared to the previous, single-threaded version of the BeepBeep engine, the
allocation of just a few threads to specific portions of a query provides
dramatic improvement in terms of running time
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
A characteristic of existing predictive process monitoring techniques is to
first construct a predictive model based on past process executions, and then
use it to predict the future of new ongoing cases, without the possibility of
updating it with new cases when they complete their execution. This can make
predictive process monitoring too rigid to deal with the variability of
processes working in real environments that continuously evolve and/or exhibit
new variant behaviors over time. As a solution to this problem, we propose the
use of algorithms that allow the incremental construction of the predictive
model. These incremental learning algorithms update the model whenever new
cases become available so that the predictive model evolves over time to fit
the current circumstances. The algorithms have been implemented using different
case encoding strategies and evaluated on a number of real and synthetic
datasets. The results provide a first evidence of the potential of incremental
learning strategies for predicting process monitoring in real environments, and
of the impact of different case encoding strategies in this setting
COST Action IC 1402 ArVI: Runtime Verification Beyond Monitoring -- Activity Report of Working Group 1
This report presents the activities of the first working group of the COST
Action ArVI, Runtime Verification beyond Monitoring. The report aims to provide
an overview of some of the major core aspects involved in Runtime Verification.
Runtime Verification is the field of research dedicated to the analysis of
system executions. It is often seen as a discipline that studies how a system
run satisfies or violates correctness properties. The report exposes a taxonomy
of Runtime Verification (RV) presenting the terminology involved with the main
concepts of the field. The report also develops the concept of instrumentation,
the various ways to instrument systems, and the fundamental role of
instrumentation in designing an RV framework. We also discuss how RV interplays
with other verification techniques such as model-checking, deductive
verification, model learning, testing, and runtime assertion checking. Finally,
we propose challenges in monitoring quantitative and statistical data beyond
detecting property violation
SURGE: Continuous Detection of Bursty Regions Over a Stream of Spatial Objects
With the proliferation of mobile devices and location-based services,
continuous generation of massive volume of streaming spatial objects (i.e.,
geo-tagged data) opens up new opportunities to address real-world problems by
analyzing them. In this paper, we present a novel continuous bursty region
detection problem that aims to continuously detect a bursty region of a given
size in a specified geographical area from a stream of spatial objects.
Specifically, a bursty region shows maximum spike in the number of spatial
objects in a given time window. The problem is useful in addressing several
real-world challenges such as surge pricing problem in online transportation
and disease outbreak detection. To solve the problem, we propose an exact
solution and two approximate solutions, and the approximation ratio is
in terms of the burst score, where is a parameter
to control the burst score. We further extend these solutions to support
detection of top- bursty regions. Extensive experiments with real-world data
are conducted to demonstrate the efficiency and effectiveness of our solutions
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