Water and physiological responses of Okra (Abelmoschus esculentus (L.) Moench) under saline stress grown on a bentonized substrate

In many arid and semi-arid regions of the world, Salinity has become an important problem for agricultural production. The objective of this study was to evaluate the effect of different treatments of NaCl (Control, 100 mM and 300 mM) for 7 days, on young okra plants (Abelmoschus esculentus), grown in two types of substrate with bentonite (B) 7% and without bentonite (WB) under controlled greenhouse conditions. The results showed that the two factors (salinity and bentonite) imposed in our study have a significant effect on the water status estimated by RWC, RWL and DHS in addition of the accumulation of the osmoregulator (proline, soluble sugars).Keywords: okra, bentonite, salinity, water parameters, biochemical parameter

Case and Activity Identification for Mining Process Models from Middleware

Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider

Finding suitable activity clusters for decomposed process discovery

Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle "big event data" adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time. Keywords: decomposed process mining, decomposed process discovery, distributed computing, event lo

pay-compensation-event-log

This data set contains event logs synthetically replicated for mining process models in big data environnements

log-replicator

This dataset contains a python program which allows to replicat the log for big data tests

pay-compensation-event-log

This data set contains event logs synthetically replicated for mining process models in big data environnements

log-replicator

This dataset contains a python program which allows to replicat the log for big data tests