Vol. 44(4), April 2015, pp. xx-xx
Research on spatial-temporal data warehouse of multidimensional marine
environment data
Jian Liu 1*, Xiangtao Fan1, Xin Zhang2, Xiaoyi Jiang3
1Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, 100094, Beijing, China 2Institute of Remote Sensing Applications, Chinese Academy of Sciences, Box.9718, DaTun Road, 100089, Beijing, China
3 National Marine Data & Information Service, No.93 Liuwei Road, Hedong District, 300171, Tianjin, China *[E-mail: [email protected]]
Received 8 November 2012; revised 10 December 2012
Present study consists the system framework and logical model of marine environment spatial-temporal data warehouse (MESTDW) for comprehensive analysis of marine environment data. Multidimensional marine environment data in this study come from “908 Chinese Coastal Oceans Investigation Project”. Multidimensional data model of comprehensive analysis subject based on star-shaped model group is formed by the data warehouse construction methods of “metadata driven - metadata sharing” and subject structure of “basic & comprehensive analytical subject” proposed in this paper. Above model and methods are verified with cases of temperature analysis, red tide monitoring and typhoon storm surge applications.
[Keywords:marine environment, spatial-temporal data warehouse, multidimensional data, data field] Introduction
A data warehouse is a subject-oriented, integrated, time-variant and nonvolatile collection of data in support of management decision making 1. In this
paper, a framework and logical model of spatial-temporal data warehouse of multidimensional marine environment data is proposed. Geographic Information System (GIS) is widely used in ocean and coastal management in China and the world2-4 and get
disciplinary knowledge from multidimensional data and applying it to coastal zone will improve the success of information systems development5.
Building the system framework and multidimensional data model suitable for marine environment data regarding data features is the main focus of this study. ArcMarine data model defines five general models, which are: Marine Points, Marine lines, Marine Area, Marine Rasters/Grids/Meshes and multimedia data as Animation/Movies/Video6. ArcMarine provides
simulation of dynamic features of data and establishes a unified data framework enabling users to modify data model for particular data and implementations7.
From the view of process simulation, Reitsma and Albrecht designed a spatial-temporal data model based on process8. Internet portals/ clearinghouses/
warehouses are developing around the world that
intends to be a source of marine and/or coastal spatial data and information9-12. Dittert et al. treat
PANGAEA Data Model as Multidimensional Data Model, so as to support SINOPS data mining, which includes dimensions like geographic dimension, sample position, time dimension, variable, and unit and so on. This data model reflects the standard steps of data procession in natural science and the standard activities of data collection in geography13. Marieke et
al. discussed a study of user requirements and defines relevant and obtainable data and information within the CoastBase project14. In design of marine fishery
data warehouse model, Su et al. and Ji et al. employed a mixed model based on star model and snow model. In their model, fishery production facts and marine environment observation facts are divided into different fact tables, and operation time, catch species, operation mode, fishing boat company and other elements are treated as the fact dimensions15, 16.
McGuire et al. designed multidimensional data model from the view of user demands, to observe spatial patterns in biological community distributions17. In
atmosphere and environment fields, researchers designed multi data models respectively through dimension decomposition and detail-level division with respect to data features, enabling them to
evaluate environment data of diverse dimensions and scales 18-20. Zubcoff et al. proposed a unified modeling
language (UML) extension through UML profiles for data mining21. Oosterom and Stoter proposes a
five-dimensional data model by adding time dimension and scale dimension to the space 3D model, to ensure the space, time and scale consistency for geography data. By using UML and object restrain language22.
Study from spatial-temporal data model to multidimensional spatial-temporal data model reflects researchers’ deepening understanding of geographical phenomenon and process, as well as endeavor to reconstruct the real world in maintaining consistency and coherence geographical phenomenon and process data in time, space and scale dimensions. Based on above researches, in this paper, thoughts and conceptual model of spatial-temporal data model are transformed into organization for datasheets of marine environmental data, to embody spatial-temporal characteristics in data organization and retrieval.
Materials and Methods Data resource and ETL method
Marine environment data resource
Data used for this study come from “908 Chinese
Coastal Oceans Investigation Project” and they are acquired in three ways: field observation, remote sensing and numerical stimulation. Data of field observation come from seashore based, ship-based and buoy monitoring. These data are stored as XML and datasheet format, with time resolutions of once, four times and eight times per day. The computing model of numerical data which has assimilated field observation data and remote sensing data has high reliability to satisfy common implementations without considering the data enormousness and structure complexity. In GIS, “object” is usually used to describe the discrete characteristic while “field” is employed to express the phenomenon of consistency in the real world23. Data products of remote sensing
retrieval and numerical model are typical marine data fields involving HDF and NetCDF formats; however, user-defined binary file format is still widely used in business unit. Compared with point and line data obtained from field observation, these data are complex in structure and enormous in data amount. The storage and analysis of data field is a focus of this study. Following table shows the sources of data field in this paper.
Table 1--Indicators of data field in this paper Product name Factors contained Geometric
features formats Product indexes marine hydrology
conventional statistical products
sea water temperature; salinity; density; speed of sound
volume unformatted binary files, text files, NetCdf files
range: globe; temporal resolution: average; grid resolution: 1° square district, north west Pacific 1/2 ° square district; vertical stratification: standard layer.
Surface meteorological conventional statistical products
sea water temperature; dew point temperature; relative humidity; temperature sea fog; precipitation, visibility; cloud amount; pressure; wind speed; wind direction; storms; swell wave height; typhoons
Surface,
volume unformatted binary files, text files, dbf files
range: globe; temporal resolution: average; grid resolution: 1° square district,(typhoon: northwest Pacific, 1/2°square district)
marine phenomenon scene-analysis
products
surface current; ocean front;
Mesoscale Vortex Surface, volume text files range: 10
°S~52°N, 99°E~150°E; grid resolution: 1/4°; products update: update daily
marine hydrology
re-analysis products sea surface height; sea water temperature; salinity; density; speed of sound; current
Surface,
volume unformatted binary files, text files
range: 10°S~52°N, 99°E~150°E; spatial resolution: horizontal 1/2°, vertical 25 layers; temporal resolution: monthly average,27 years(1986-2012)
Marine nowcast,
forecast products sea surface height; sea water temperature; salinity; density; Surface, volume unformatted binary files, range: 10
°S~52°N, 99°E~150°E; spatial resolution: horizontal 1/2°, standard layer;
speed of sound; currents; tidal; storm surge water-level field
text files, xml
files temporal resolution: the water level and current hourly, the remaining factors are daily on average; now-cast products: update daily; forecast products: 7-day forecast, updated daily
Key steps of marine environmental data field ETL
Different from point and line data, 2D and 3D data field have the characteristics of large data amount and complex structure. Therefore, quality check and integration methods of data field are discussed in this section.
Key step1--Marine environmental data quality check Limit value check for single file and internal consistency check for a group of files are main steps of data quality check. Limit value check is to check whether the element value is within its permit value range or not. For instance, sea water temperature is usually from -2℃ to 30℃, and this check is usually
done by sampling. Limit value changes with regions and seasons. Limit value check is to make sure whether or not a certain factor value exceeds its maximum value or minimum value. It is to be noted that as climate changes, some climate extreme values are broken. Thus check of data exceeding extreme values must be taken on the basis of the manual review to determine whether the data is correct and modify the original extreme value parameters timely. The internal consistency check is to check whether there exists certain law among projects in observational data or some physical characteristics associated with weather elements. The internal consistency check can be divided into two types. One is the consistency of different projects of the same element at the same time, and the other is the consistency of different element at the same time. As one station's weather and climate characteristics are described by various elements from different aspects, there exist associations among different elements of the same time.
Key step2--Data Field integration and storage. The diversity of formats brings challenge to the effective integration of marine environmental data. The logical flow of data integration of heterogeneous data field is shown in Fig. 1. First, heterogeneous data field are converted into exchange format with format conversion program. 3D data field needs to be split by layer, and a layer of data at each moment of each element is saved as a file; then, metadata information is added for each set of data field, and data in the data
preparation area are stored in categories and groups in the form of “a metadata file & a set of data files "; Finally, data updating is checked with automatic update service, and data are loaded into the appropriate location of the data warehouse according to the metadata, and data are stored in the Oracle database ultimately in BFile format. In current marine and meteorological data warehouses, three dimensional data field are discrete to points to be stored. Direct storage of the grid point values and latitude and longitude information will cause dramatic increase of data records number and cause query difficulties.
Fig. 1—grid data field integration process
The advantages to split data by layer are as follows: 1) as most of the three-dimensional data is now organized by layer, it not only fits the 3D data field production model, but also conform with the data analysis habits of business staff; 2) it will greatly reduce the number of records of the fact table in data warehouse, and improve the efficiency of data retrieval; 3) it is convenient to carry on drilling,
Data warehouse
Format conversation
HDF、NetCDF、binary files 、text files、
datatables……. 2D data field Original data
3D data field
Static 2D data field
…… t1 t2 …… tn
…… t1 t2 …… tn Client
Dynamic 2D data field
Static 3D data field Dynamic 3D data field Split by layer
Binary files
slicing and cutting operations, having an easy access to a single grid point value, and its time sequence and depth sequence value; To generate such as single-layer elements field and its spatial-temporal process analysis data, and multi-layer elements field and its spatial-temporal process analysis data, and so on.
MESTDW framework research
The subject structure of MESTDW
No matter research of what marine activities, the object of data warehouse is to provide marine environmental data and associated properties for different applications for users of different levels. Commonly used environmental factors include temperature, salinity, density, waves, currents, weather elements, trends, tides, sea water level and so on. Analysis and identification of subject to be loaded into the data warehouse is the first step of information package technology. In this research, commonly used marine environmental factors are treated as basic subjects. At first, basic subjects are created, and the fact tables are designed on the basis of basic analysis subjects; then, a comprehensive analysis subject is established according to specific analysis demands (Fig. 2).
Fig. 2—subject structure of MESTDW
According to specific analysis requirements, the fact table of comprehensive analysis subject is formed by connecting different fact tables of basic analysis subjects. A multi-dimensional data cube is formed through shared dimension table to provide comprehensive analysis subject with respect to different marine activities or marine researches, and to form a flexible and efficient subject structure of marine environment. Comprehensive analysis subject is raised for data integration implementation, which is the advanced implementation of data warehouse.
Data warehouse construction method
In the field of marine research, marine metadata standards include MEDI (Marine Environmental Data and Inventory), EDIOS (European Directory of the Ocean-observing System) and ODAS (Ocean Data Acquisition System) and so on. Research and application of marine metadata technology have also made rapid development in China, such as WDC-D Earth Sciences database system- metadata of marine disciplines basic database, sustainable sharing of information metadata (http://sdinfo.coi.gov. cn/index0.html) and so on. In this paper, metadata of MESTDW are organized in accordance with hierarchy, involving metadata subset, metadata entities and metadata elements. Based on different copes, marine environmental metadata can be divided into three subsets: extraction and transformation metadata, operation metadata and management metadata. The main source of data warehouse extraction and transformation metadata and operation metadata comes from the metadata of “908 Chinese Coastal Oceans Investigation Project”. Before data loading, these data are stored in XML format with metadata generation tools.
Extraction and transformation metadata are mainly used in the data acquisition phase as the basis for data extraction, transformation, cleaning, loading, and they are all must-entities. Extraction and transformation metadata should consist of data sources, classification, and time range of materials, update frequency, data structure and data quality entities.
Operation metadata mainly serves different users to query, retrieve data and create their own applications. Operation metadata mainly come from marine environmental metadata, and the entities consist of data time (time resolution), spatial extent, and access-restrict information, browsing information, name of source items, relevant data sets, spatial resolution and spatial reference.
Management metadata provide services for data warehouse operation monitoring management, detail management, user management, and role management and so on. Entities of management metadata include server information, user information, role information, data dictionary, data warehouse refreshing information, operation logs, error logs, management function library, metadata description information and version management.
In the design of data warehouse data model, data is divided into different subjects of analysis by connecting dimension tables to build a data cube and implement multidimensional analysis. There may be
duplication or inconsistency if data dimension table is built with respect to separate analysis subject, and in this case data integrated queries can not be effectively supported. In this paper, the data warehouse construction method of “metadata driven - metadata dimension sharing” is proposed. Based on the metadata information of marine environmental spatial-temporal data, the demands for data applications are met with complete metadata in various stages of data warehouse construction. In the storage of multidimensional data, each type of metadata entity corresponds to one or several data dimension table, i.e. metadata entities are the dimension construction basis of multi-dimensional model. By sharing of dimension table, all the analysis subjects get a unified data dimension. By connecting different fact tables and shared dimension, a data cube is generated regarding various analysis subjects to meet the needs of integrated query and analysis of
data. The use of a shared and standardized dimension will bring enormous benefits to improve the structure and performance of the entire data warehouse.
The overall structure of MESTDW
The system framework of MESTDW is shown in Fig. 3. In data acquisition layer, source data are added into data preparation area after extraction, conversion, quality checking and metadata information adding, and data are automatically discovered and updated to data warehouse through data updating service. In data storage layer, data are formed into multidimensional data cube according to analysis subjects. In analysis layer, online analytical processing (OLAP) server provides a variety of analytical processing functions and tools, including query tools, multidimensional analysis tools, and visualization tools etc. It also has easy graphical interface and are intuitively provided to managers and decision-makers.
Fig. 3—overall structure of MESTDW
Marine environmental data are distributed in the Institute of Marine Research and the Oceanic Administrations of China’s coastal provinces, Chinese
National Marine Information Centre. These data are in geographically distributed storage situation. In addition, for reasons of data confidentiality or data
Data node Data ETL process Data Extraction Data Transform Metadata Edition Data market Metadata database Metadata management Dynamic visualization of 2D, 3D data
Comprehensive analysis & visualization
Classification, cluster, feature extraction, regression, deviation, evolution Basic operation of data field OLAP operations
Data node
Local data analysis Local data warehouse warehouse Dimensional tables Fact tables Subject1
Data warehouse management
…
Data market
Data market
Subject2 Subject3 Subjectn Archival data Related data t Specific datat Operational system Quality Inspection
Automatic data update service
XML metadata data files 1…n
Data prepare area
Data node
Local data analysis Local data warehouse warehouse
DM DM
amount and others, all these data cannot be stored together. Therefore, MESTDW can only be a distributed data warehouse system, and each node has certain requirements for data analyzing and processing. Local data warehouse is set up for applications of data nodes, which contains historical data and integrated data on the local node, and each local data warehouse has its own data and processor, and so on. Main data warehouse faces particular departments or applications. For instance, sea weather forecasts, storm surge hazard assessment and provide analysis data to form sector-level data mart. Moreover, based on the demands of every data node, local data warehouse or data preparation area can be established, as the data source of main data warehouse.
Multidimensional data model of MESTDW
In this paper, the star schema family structure is employed for logic model design, and all the marine spatial-temporal data are stored in commercial relational database in accordance with relational model.
Fact table building
In MESTDW, the measure value of fact table can be the value of temperature, salinity, flow rate, flow direction and so on. When build multidimensional data model of basic analytical subject, the columns of fact table except metric variables are all primary keys (hour ID, region ID, point ID, contact information ID, project ID, survey information ID, data category code, data format ID and spatial reference identifier) of each dimension table, and they are foreign keys in fact table and can not be empty. One analysis subject may correspond to multiple fact tables. For example, the fact table of temperature, salinity, and density subjects includes seasonal statistical fact table, monthly statistical fact table and the fact table of time data; tidal subject corresponds to the fact tables of tidal site observation and tidal large-area forecast data. A fact table may also belong to multiple analytical subjects, for instance, the subjects of temperature, salinity and density can be used for various subjects as fishery resources analysis, red tide analysis and so on.
Dimensions modeling and hierarchy identification
of MESTDW
Time-varying and multiple spatial-temporal hierarchies are the most prominent features of marine environment data, and space dimension and time dimension are the basis to reflect dynamical changes in the real world. Time data for marine environment
contains the observation time, analysis time, statistical time and forecast time. In addition, the time accuracy varies. For instance, some of the statistical data use ten days as a unit and others use year or month, while analysis data and observation data are generally in hours. The valid time of the marine environmental data adopts the time hierarchy of hour → day →ten days→month→quarter→year, to ensure the integrity of the data in granularity level. The structure of time dimension table is shown in Fig. 4. In MESTDW, regardless of the type of granularity, measure of fact table must have the same level of detail. It will confuse users as well as make data analysis and application fallible if the facts representing granularity of multiple levels are mixed, or data of multiple granularity are in mixed storage. Therefore, time dimensional table consists of interrelated tables as year table, quarter table, month table, ten days table, day table and hour table. Each time level can aggregate up to any of its above level, and whenever data of new granularity level are generated, there will be fact table of corresponding granularity level.
Fig.4—structure of time dimensional table of MESTDW
From the real world to GIS world, a conversion must be carried on from continuous space to discrete space for geographical space. Spatial expression of the marine environment data is actually a discretization and re-sampling process of the ocean. In MESTDW, the point and line data from observation stations, survey line and underway data are denoted with points(x, y, z) of latitude, longitude and depth, and then values on these points are
recorded. The space of multi-layer data is obtained by splitting three-dimensional volume data, remote sensing information products, and other large-area analysis products is denoted with name, scope of coordinate and depth. The content of space dimension table comes from the space spatial extent information of metadata and navigation map information. In MESTDW, the hierarchy structure of spatial dimension table is determined by grid resolution of data, and the roll-up and drill-down in space dimension are actually polymerization and further subdivision to spatial grid resolution of data.
Fig.5—Structure of spatial dimension table of MESTDW
The project source dimension and contact information dimension are important basis for data retrospection, which comes from source project entities and data source entities of metadata. Survey information dimension records information of data observation instruments and platforms, which comes from survey information entities. The dimension of element types comes from data classification entity. In type dimension, roll-up operation can be performed on data according to the hierarchy of type code, so as to get the data information of the major categories to which it belongs, and drill-down operation can also be carried on for next category. Due to the differences of data sources and acquisition methods, data of the same subject may have various formats in fact table. The format and using information of data are recorded in data structure dimension, which makes it easy for program to take on automatic identification and processing. Data structure dimension comes from data format entities of metadata. Space reference information is recorded in spatial reference dimension
which comes from spatial reference entities of metadata.
Results and Discussion
Multidimensional data model of MESTDW
By connecting fact table with dimension table, a multidimensional data structure of analysis subject can be obtained. In this section, several cases were taken to study the multidimensional model of basic analysis subject and comprehensive subject.
Data model of basic temperature analysis subject The fact table of temperature consists of numerical simulate data and observed data of different time levels. The temperature value of certain point is directly recorded in the fact table of observed temperature; in numerical simulate temperature fact table, the type of temperature field is BFile in Oracle, which records data field of regional temperature, graphical files, and the data connection information (likes WMS service address and parameter information). Multidimensional data model of regional temperaturesubject is shown Fig. 6, in which the detail level of fact table is “quarter”, and the data in this detail level can be aggregated up to fact table of levels of year.
Fig.6—Multidimensional structure of temperature regional subject
The temporal-spatial visualization of marine environmental data is the process of drilling a sequence data from data cube in central data warehouse and generates images. The drill method is: First, obtain a time series of sea water temperature data from data cube. According to user defined conditions, to query the time, region, source etc. dimension tables. IDs will be got as new query conditions to get a time series of temperature data field in the temperature fact table. Get data format information from data format table according to the format ID got at first step; Call the appropriate
visualization methods according to the data format. Visualization of sea water temperature data is based on snap-shot spatial-temporal model. The colorized image is used for the expression of temperature data field. Smooth and syncretize each vertex’s color of the grid cell, that is, give an array of color to each vertex to form a gradual changing color image. Dynamical visualization of sea water temperature is realized by displaying data of time series (Fig. 7).
Fig.7—Time series of seawater surface temperature data (a) First quarter, (b) Second quarter, (c) Third quarter, (d) Fourth quarter.
Three dimensional scalar fields can be sliced along longitude, latitude, depth and at any angles (fig.8). By comparing the position of slice in the body of elements, if the slice falls between two grids, then interpolation is needed at the slice position to calculate the value of the slice.
Fig.8 —Profile of temperature data field. (a) Slice of depth ,-50 meters deep, (b) Slice of latitude 31.5°N, (c) Slice of
longitude,138.75°E, (d) Slice of any angle.
Structure and application of multidimensional data from red tide monitoring
Red tide is one of the marine disasters puzzling a great many coastal countries. It is a complex ecological anomaly with complex causes. Testing results of red tide show that the water of these areas has suffered serious pollution, which is eutrophicated with nitrogen and phosphorus and other nutrient substances greatly exceeded. Changes of hydro-meteorological and sea water physical and chemical factors is an important cause of red tide. Seawater temperature is an important environmental factor of red tide occurrence, and scientists find that within a week the water temperature suddenly increased for
more than 2 ℃ is the precursor to red tide occurrence.
Seawater chemical factors such as salinity changes are also causes to the mass rearing of red tide organism. However, when the salinity ranges from 15 to 21.6, the thermocline and halocline are easy to form, which provides conditions for red tide organism to aggregate. In addition, according to monitoring data, when red tide occurs, the aquatic environment over these waters is often drought and hot, with high water temperature, weak wind and slow trend, and so on.
Fig. 9—multidimensional structure of red tied comprehensive analysis subject
When analyze the occurrence environment of red tide, the regional data with time scale of hours of temperature, salinity, tide and tide current are selected, combined with biological fact and chemical fact data (types of analysis of biological and chemical elements are selected through element type table) to form multidimensional data model of comprehensive analysis subject of red tide occurrence environment. The multidimensional structure of red tide is shown in fig. 9, tables in the red dotted box are necessary dimension tables and tables in red oval are related fact tables. Multidimensional analysis is carried out between the fact tables and the dimension tables of time, space and region, to restore the multidimensional data cube (Fig. 10). Slice and dice operations are performed to this cube, so as to acquire the data interested.
Fig.10—Multidimensional data operation of red tide comprehensive analysis subject
Multidimensional data structure and applications of storm surge
China is the country suffering storm surge disasters most frequently and seriously, with almost all the coastal areas been stricken. The real analysis data of storm surge generated from coastal data nodes is uploaded real-timely to the main node of data warehouse through network. The data packet consists of water lever data field of storm surge, typhoon path information and XML metadata files. After detecting data, data center extracts and uploads it to the corresponding table in the data warehouse with data update service.
The multidimensional structure of storm surge comprehensive analysis subject is consist of fact tables such as storm surge water level data table, typhoon path table and dimension tables such as time (h), spatial point and spatial region table etc. The fact table of storm surge is used to record the measure values at the intersections of all dimensions, and it is manifested as the water level value of storm surge. Typhoon fact table records the number of typhoon, wind speed, direction, and the coordinate of longitude and latitude information. Time dimension table records the occurrence time, effective time and object time of data. Storm surge occurs in a period of time, and there are various time intervals for forecasting and analysis such as one hour, four hours, six hours, and so on. Space dimension table records the range name and coordinate information of data according to the occurrence area of storm surge. Information dimension table records the element type (values include: prediction, reanalysis, live analysis), format type and grid resolution. Metadata dimension table records the project source, producing department,
producer, auditor and other information.
In the process of visualized analysis of data, the water level data field of storm surge is manipulated to be texture images, and typhoon path information may be added in the form of symbols if needed. The storm surge process is simulated with dynamic changes of a time series (Fig. 11).
Fig.11— Comprehensive visualization of storm surge process
Conclusion
For the integrated management and application of marine environmental data, a system structure and multidimensional data model of MESTDW is proposed in this paper. In the ETL process, an approach based on exchange format and hierarchical storage is employed for the storage of data field, which to some extent solves the problem of multi-source and heterogeneous data field integration. Data warehouse system framework was built with data warehouse construction method of “metadata driven-metadata dimension sharing” and subject structure of “basic & comprehensive analysis subject”. Metadata entity is the basis for dimension modeling of multidimensional data model, which gives the unified dimension to analysis subject. A multidimensional data model of comprehensive analysis subject based on star-shaped model group is formed by connecting basic fact table with shared dimensional table. The marine environmental elements in the MESTDW includes: Marine hydrological, meteorological, biological chemical elements and so on. The types of data products include: Real time observation, reanalysis, live-analysis, forecast and statistical data, and the total data amount is TB level. Multi-dimensional data model is certificate by dynamical visualization in prototype system.
Since this research involves many branches of learning and directions, what we do in this study is just a preliminary exploration in this field. Considering the inadequacies of this paper, some areas deserving further research are suggested as follows: In this paper, logical model of the MESTDW is designed. Data block and hierarchical indexing strategy, data parallel operations, etc still need to be
studied according to data characteristics. The marine environmental objects indicated in this paper are basic objects. Since the object hierarchy is relatively simple, there is a need to expand the types of spatial objects based on operational running. Multidimensional data visualization for marine environmental facts: to introduce visual data mining method to the expression of marine 3D visualization, not only intuitive data visualization but also correlative relationship and cluster relations expression.
Acknowledgement
This work is supported by National Natural Science Foundation of China (No.41201399), the director foundation of institute of remote sensing and digital earth, Chinese Academy of Science (Y3SJ5800CX), and the open foundation of State Key Laboratory Program of Digital Ocean, SOA (No. KLDO201301).
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