1,791 research outputs found
Query Stability in Monotonic Data-Aware Business Processes [Extended Version]
Organizations continuously accumulate data, often according to some business
processes. If one poses a query over such data for decision support, it is
important to know whether the query is stable, that is, whether the answers
will stay the same or may change in the future because business processes may
add further data. We investigate query stability for conjunctive queries. To
this end, we define a formalism that combines an explicit representation of the
control flow of a process with a specification of how data is read and inserted
into the database. We consider different restrictions of the process model and
the state of the system, such as negation in conditions, cyclic executions,
read access to written data, presence of pending process instances, and the
possibility to start fresh process instances. We identify for which facet
combinations stability of conjunctive queries is decidable and provide
encodings into variants of Datalog that are optimal with respect to the
worst-case complexity of the problem.Comment: This report is the extended version of a paper accepted at the 19th
International Conference on Database Theory (ICDT 2016), March 15-18, 2016 -
Bordeaux, Franc
Temporal Stream Algebra
Data stream management systems (DSMS) so far focus on
event queries and hardly consider combined queries to both
data from event streams and from a database. However,
applications like emergency management require combined
data stream and database queries. Further requirements are
the simultaneous use of multiple timestamps after different
time lines and semantics, expressive temporal relations between multiple time-stamps and
exible negation, grouping
and aggregation which can be controlled, i. e. started and
stopped, by events and are not limited to fixed-size time
windows. Current DSMS hardly address these requirements.
This article proposes Temporal Stream Algebra (TSA) so
as to meet the afore mentioned requirements. Temporal
streams are a common abstraction of data streams and data-
base relations; the operators of TSA are generalizations of
the usual operators of Relational Algebra. A in-depth 'analysis of temporal relations guarantees that valid TSA expressions are non-blocking, i. e. can be evaluated incrementally.
In this respect TSA differs significantly from previous algebraic approaches which use specialized operators to prevent
blocking expressions on a "syntactical" level
Towards an ontology for process monitoring and mining
Business Process Analysis (BPA) aims at monitoring, diagnosing, simulating and mining enacted processes in order to support the analysis and enhancement of process models. An effective BPA solution must provide the means for analysing existing e-businesses at three levels of abstraction: the Business Level, the Process Level and the IT Level. BPA requires semantic information that spans these layers of abstraction and which should be easily retrieved from audit trails. To cater for this, we describe the Process Mining Ontology and the Events Ontology which aim to support the analysis of enacted processes at different levels of abstraction spanning from fine grain technical details to coarse grain aspects at the Business Level
Algebraic Approach to Timed Petri Nets
One aspect often needed when modelling systems of any kind is time-based analysis, especially for real-time or in general time-critical systems. Algebraic place/transition (P/T) nets do not inherently provide a way to model the passing of time or to restrict the firing behaviour with regards to passing time. In this paper, we present an extension of algebraic P/T nets by adding time durations to transitions and timestamps to tokens. We define categories for different timed net classes and functorial relations between them. Our first result is the definition of morphisms preserving firing behaviour for all timed net classes. As second result, we define structuring techniques for timed P/T nets in a way that our category fulfills the properties of M-adhesive systems, a general categorical framework for structuring and transforming high-level algebraic structures. We demonstrate our approach by applying it to model a real-time communication network
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances
Predicting the completion time of business process instances would be a very
helpful aid when managing processes under service level agreement constraints.
The ability to know in advance the trend of running process instances would
allow business managers to react in time, in order to prevent delays or
undesirable situations. However, making such accurate forecasts is not easy:
many factors may influence the required time to complete a process instance. In
this paper, we propose an approach based on deep Recurrent Neural Networks
(specifically LSTMs) that is able to exploit arbitrary information associated
to single events, in order to produce an as-accurate-as-possible prediction of
the completion time of running instances. Experiments on real-world datasets
confirm the quality of our proposal.Comment: Article accepted for publication in 2017 IEEE Symposium on Deep
Learning (IEEE DL'17) @ SSC
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