This paper presents an approach to predicting the productivity of the concreting process based on a conducted quantitative research involving the recording of concreting at building construction sites in the city of Nis, Serbia. In the period of 20 months, 81 recordings of reinforced slabs on eight construction sites of buildings were observed and recorded. The total amount of poured concrete was 11951 m 3 and the total consumed time was 503 work hours. The factors that could impact productivity have been identified and a simulation model for predicting the productivity of the concreting process has been developed using Discrete Event Simulation and Agent Based Modelling. AnyLogic software package was used to develop the simulation model. Experiments were carried out and based on the obtained parameters the models are estimated. The proposed models can be useful in the planning stage and allow for more precise prediction of concreting productivity, thus benefiting the decision making and work flow prediction and improving the concreting process management in order to increase productivity, shorten the delays, and reduce costs.
Intrusion detection system (IDS) is one of the most important components
being used to monitor network for possible cyber-attacks. However, the
amount of data that should be inspected imposes a great challenge to IDSs.
With recent emerge of various big data technologies, there are ways for
overcoming the problem of the increased amount of data. Nevertheless, some
of this technologies inherit data distribution techniques that can be a
problem when splitting a sensitive data such as network data frames across a
cluster nodes. The goal of this paper is design and implementation of Hadoop
based IDS. In this paper we propose different input split techniques
suitable for network data distribution across cloud nodes and test the
performances of their Apache Hadoop implementation. Four different data
split techniques will be proposed and analysed. The techniques will be
described in detail. The system will be evaluated on Apache Hadoop cluster
with 17 slave nodes. We will show that processing speed can differ for more
than 30% depending on chosen input split design strategy. Additionally,
we?ll show that malicious level of network traffic can slow down the
processing time, in our case, for nearly 20%. The scalability of the system
will al so be discussed.
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