13,984 research outputs found
Predictive Monitoring of Business Processes
Modern information systems that support complex business processes generally
maintain significant amounts of process execution data, particularly records of
events corresponding to the execution of activities (event logs). In this
paper, we present an approach to analyze such event logs in order to
predictively monitor business goals during business process execution. At any
point during an execution of a process, the user can define business goals in
the form of linear temporal logic rules. When an activity is being executed,
the framework identifies input data values that are more (or less) likely to
lead to the achievement of each business goal. Unlike reactive compliance
monitoring approaches that detect violations only after they have occurred, our
predictive monitoring approach provides early advice so that users can steer
ongoing process executions towards the achievement of business goals. In other
words, violations are predicted (and potentially prevented) rather than merely
detected. The approach has been implemented in the ProM process mining toolset
and validated on a real-life log pertaining to the treatment of cancer patients
in a large hospital
Dependability Analysis of Control Systems using SystemC and Statistical Model Checking
Stochastic Petri nets are commonly used for modeling distributed systems in
order to study their performance and dependability. This paper proposes a
realization of stochastic Petri nets in SystemC for modeling large embedded
control systems. Then statistical model checking is used to analyze the
dependability of the constructed model. Our verification framework allows users
to express a wide range of useful properties to be verified which is
illustrated through a case study
Inferring Concise Specifications of APIs
Modern software relies on libraries and uses them via application programming
interfaces (APIs). Correct API usage as well as many software engineering tasks
are enabled when APIs have formal specifications. In this work, we analyze the
implementation of each method in an API to infer a formal postcondition.
Conventional wisdom is that, if one has preconditions, then one can use the
strongest postcondition predicate transformer (SP) to infer postconditions.
However, SP yields postconditions that are exponentially large, which makes
them difficult to use, either by humans or by tools. Our key idea is an
algorithm that converts such exponentially large specifications into a form
that is more concise and thus more usable. This is done by leveraging the
structure of the specifications that result from the use of SP. We applied our
technique to infer postconditions for over 2,300 methods in seven popular Java
libraries. Our technique was able to infer specifications for 75.7% of these
methods, each of which was verified using an Extended Static Checker. We also
found that 84.6% of resulting specifications were less than 1/4 page (20 lines)
in length. Our technique was able to reduce the length of SMT proofs needed for
verifying implementations by 76.7% and reduced prover execution time by 26.7%
Big Data Caching for Networking: Moving from Cloud to Edge
In order to cope with the relentless data tsunami in wireless networks,
current approaches such as acquiring new spectrum, deploying more base stations
(BSs) and increasing nodes in mobile packet core networks are becoming
ineffective in terms of scalability, cost and flexibility. In this regard,
context-aware G networks with edge/cloud computing and exploitation of
\emph{big data} analytics can yield significant gains to mobile operators. In
this article, proactive content caching in G wireless networks is
investigated in which a big data-enabled architecture is proposed. In this
practical architecture, vast amount of data is harnessed for content popularity
estimation and strategic contents are cached at the BSs to achieve higher
users' satisfaction and backhaul offloading. To validate the proposed solution,
we consider a real-world case study where several hours of mobile data traffic
is collected from a major telecom operator in Turkey and a big data-enabled
analysis is carried out leveraging tools from machine learning. Based on the
available information and storage capacity, numerical studies show that several
gains are achieved both in terms of users' satisfaction and backhaul
offloading. For example, in the case of BSs with of content ratings
and Gbyte of storage size ( of total library size), proactive
caching yields of users' satisfaction and offloads of the
backhaul.Comment: accepted for publication in IEEE Communications Magazine, Special
Issue on Communications, Caching, and Computing for Content-Centric Mobile
Network
Formal Verification of Probabilistic SystemC Models with Statistical Model Checking
Transaction-level modeling with SystemC has been very successful in
describing the behavior of embedded systems by providing high-level executable
models, in which many of them have inherent probabilistic behaviors, e.g.,
random data and unreliable components. It thus is crucial to have both
quantitative and qualitative analysis of the probabilities of system
properties. Such analysis can be conducted by constructing a formal model of
the system under verification and using Probabilistic Model Checking (PMC).
However, this method is infeasible for large systems, due to the state space
explosion. In this article, we demonstrate the successful use of Statistical
Model Checking (SMC) to carry out such analysis directly from large SystemC
models and allow designers to express a wide range of useful properties. The
first contribution of this work is a framework to verify properties expressed
in Bounded Linear Temporal Logic (BLTL) for SystemC models with both timed and
probabilistic characteristics. Second, the framework allows users to expose a
rich set of user-code primitives as atomic propositions in BLTL. Moreover,
users can define their own fine-grained time resolution rather than the
boundary of clock cycles in the SystemC simulation. The third contribution is
an implementation of a statistical model checker. It contains an automatic
monitor generation for producing execution traces of the
model-under-verification (MUV), the mechanism for automatically instrumenting
the MUV, and the interaction with statistical model checking algorithms.Comment: Journal of Software: Evolution and Process. Wiley, 2017. arXiv admin
note: substantial text overlap with arXiv:1507.0818
- …