A DATA MINING APPROACH TO FORECAST LATE ARRIVALS IN A TRANSHIPMENT CONTAINER TERMINAL

One of the most important issues in Transhipment Container Terminal (TCT) management is to have fairly reliable and affordable predictions about vessel arrival. Terminal operators need to estimate the actual time of arrival in port in order to determine the daily demand for each work shift with greater accuracy. In this way, the resources required (human resources, equipment as well as spatial resources) can be allocated more efficiently. Despite contractual obligations to notify the Estimated Time of Arrival (ETA) 24 hours before arrival, ship operators often have to revise it due to unexpected events like weather conditions, delay in a previous port and so on. For planners the decision-making processes related to this topic can sometimes be so complex without the support of suitable methodological tools. Specific models should be adopted, in a daily planning scenario, to provide a useful support tool in TCTs. In this study, we discuss an exploratory analysis of the data affecting delays registered at a Mediterranean TCT. We present some preliminary results obtained using data mining techniques and propose a Classification and Regression Trees (CART) model to reduce the range of uncertainty of ship arrivals in port. This approach is compulsory to manage vast amounts of unstructured data involved in estimating of vessel arrivals.

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Temporal Analysis for Epileptic Seizure Detection by Using Data Mining Approach

2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) ◽

10.1109/hpcc-smartcity-dss50907.2020.00175 ◽

2020 ◽

Author(s):

Francesca Pafferi ◽

Gaetano Zazzaro ◽

Angelo Martone ◽

Paolo Bifulco ◽

Luigi Pavone

Keyword(s):

Data Mining ◽

Epileptic Seizure ◽

Temporal Analysis ◽

Seizure Detection ◽

Epileptic Seizure Detection ◽

Data Mining Approach ◽

Using Data

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Predicting car purchase intent using data mining approach

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) ◽

10.1109/fskd.2011.6019863 ◽

2011 ◽

Cited By ~ 1

Author(s):

Yap Bee Wah ◽

Nor Huwaina Ismail ◽

Simon Fong

Keyword(s):

Data Mining ◽

Purchase Intent ◽

Data Mining Approach ◽

Using Data

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A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities

Journal of Computing and Information Science in Engineering ◽

10.1115/1.1812556 ◽

2004 ◽

Vol 4 (4) ◽

pp. 316-328 ◽

Cited By ~ 34

Author(s):

Carol J. Romanowski , ◽

Rakesh Nagi

Keyword(s):

Data Mining ◽

Product Family ◽

Research Direction ◽

Design Constraints ◽

Variant Design ◽

Data Mining Approach ◽

Design Activities ◽

Using Data ◽

New Research

In variant design, the proliferation of bills of materials makes it difficult for designers to find previous designs that would aid in completing a new design task. This research presents a novel, data mining approach to forming generic bills of materials (GBOMs), entities that represent the different variants in a product family and facilitate the search for similar designs and configuration of new variants. The technical difficulties include: (i) developing families or categories for products, assemblies, and component parts; (ii) generalizing purchased parts and quantifying their similarity; (iii) performing tree union; and (iv) establishing design constraints. These challenges are met through data mining methods such as text and tree mining, a new tree union procedure, and embodying the GBOM and design constraints in constrained XML. The paper concludes with a case study, using data from a manufacturer of nurse call devices, and identifies a new research direction for data mining motivated by the domains of engineering design and information.

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