Study On Oceangoing Ship Scheduling Data Mining Technology And Its Application

As an important step in the knowledge discovery, data mining is the process of extracting unknown, valuable and workable relationship, patterns and trends from the large-scale database and data warehouse for decision-making supporting. As the rapid development of the ship-shore communications technology and computer storage devices , ship scheduling data emerges in shipping enterprises, so how to make full use of data mining technology to analyze the implicit rule from the ship scheduling data is one concerned issue of intelligent transportation on sea. Combining with the characteristics of association analysis, data reduction, acquisition of decision-making rules, etc., this dissertation mainly researches on the application of data mining technology on ocean-going vessels scheduling problems, and discusses the application of those methods to the analysis of global port cargo handling, analysis of cargo shipping routes, analysis of fuel consumption for ship sailing. For a more effiective data mining analysis, this dissertation designs and implements the ship scheduling data warehouse, demonstrates its applications. Ship scheduling data warehouse and its applications are integrated into the ship scheduling system. The research contents and results of this dissertation as follows:(1) Through the study of China's shipping companies scheduling operations, this dissertation establishes the differenet themes in ship sheduling data warehouse, including theme of global port cargo handling, theme of cargo flows and voyage, theme of energy-saving,etc. The structure, model, function, data storage model and realization of the ship scheduling data warehouse are all studied to manage and analyze the massive ship scheduling data, which offers data support for follow-up data mining algorithms. Subsequently, the ship scheduling data mining system, which includes data layer, organization layer, mining layer and decision layer, is established. Different layer has its own functions, from data pre-process, data mining to knowledge expression, under differenet stages of data mining task to formulate a whole system. (2) Aim at resolving the problem of repeatedly accessing the data table for mining association rule, this dissertation analyses the relation between rough set and association rule, then proposes a multi-dimensional association algorithm based on equivalent category in rough set. In this algorithm, the computing of multi-dimensional frequent items is converted to computing of equivalent category with multi-attributes. So, the number and content of multi-dimensional frequent items and association rules produced by this algorithm are limited by interesting dimensions which are assigned by uesr. Compared with Apriori algorithm, this algoritm reduces the number of accessing and scaning database. So this algorithm decreases the time of computing association rules and is efficient.(3) This dissertation researches on the application of the multi-dimensional data mining association rules algorithm in the analysis of cargo flow and ship routes. The essence of oceangoing ship transportation is the changes of cargoes position under time and space dimensions. In a voyage, ship may load many kinds of cargo at the same port and discharge those cargoes in different ports. Concernd this, the cargo dimension data is pre-processed and converted to information system. Then, the interesting rules concerned ship type-ship route and cargo category-ship route are obtained by the multi-dimensional data mining association rules algorithm proposed by this dissertation, which is applied to research the relations of ship-route,ship-type,cargo and time.(4) Positive region is a key concept in rough set and plays an important role in calculating the dependency degree of attributes, the ability of classification and the significance of attributes. A new improved algorithm of calcualting positive region is proposed by this dissertation. The new algorithm deletes the compared objects timely and cuts down the combinations of object pairs for next computing. Experiments on data sets from UCI show that the new algorithm on attribute reduction is more efficient than classical algorithm of calculating positive region, especially on large data sets.(5) It is well known that finding the shortest reduct is NP hard. In this dissertation, a novel heuristic algorithm based on the ability of classification is proposed for attribute reduction. In the new algorithm, cardinality attributes is used as the heuristic. Compared with the positive region calculating algorithm, the new algorithm calculates the ability of classification, instead of generating positive region. Experiments on data sets from UCI show that the new algorithm is more efficient on attribute reduction in decision information system.(6) The process of fuel consumption for ship sailing is complicated and easily influenced by many factors. In fact, some attributes of fuel consumption miss values. In this dissertation, incomplete fuel consumption information system is firstly transformed into complete information system. In order to get the valuable decision rules and support the decision-making on energy saving, the new improved alogorithm of calculating positive region is used to computing the significance of the differnet fuel consumption factors and attribute reduction algorithm is used to compute the redcut of fuel consumption factors.Finally, the conclusion is made, and the problems for further study are reviewed.