CN101882164A - Data warehouse model for storing multidimensional knowledge - Google Patents
Data warehouse model for storing multidimensional knowledge Download PDFInfo
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- CN101882164A CN101882164A CN2010102284137A CN201010228413A CN101882164A CN 101882164 A CN101882164 A CN 101882164A CN 2010102284137 A CN2010102284137 A CN 2010102284137A CN 201010228413 A CN201010228413 A CN 201010228413A CN 101882164 A CN101882164 A CN 101882164A
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Abstract
The invention relates to a data warehouse model for storing multidimensional knowledge. The data warehouse model comprises an absolute dimension layer, an aggregation layer, a relative dimension layer and an entity layer, wherein the absolute dimension layer comprises an absolute time dimension module, an absolute place dimension module and a main classification dimension module; the aggregation layer comprises an event module, an organization module and a relationship module; the relative dimension layer comprises a relative time dimension layer, a relative place dimension module and a role dimension module; and the entity layer comprises a personnel module, an article module and a contact way module. The data warehouse model has the advantages of combining various kinds of multidimensional knowledge such as the time module, the place module, a classification module and the like and a plurality of aggregations such as events, organizations, family relationships, call records, flight numbers, hotels and the like, realizing knowledge discovery, realizing knowledge limitless association and finding the required knowledge rapidly.
Description
Technical field
The present invention relates generally to data warehouse and data mining field, relates in particular to a kind of data warehouse model that is used to store multidimensional knowledge.
Background technology
The data warehouse major function is with government, the mass data that tissues such as enterprise are accumulated year in year out by online trade system (OLTP), by the theoretical peculiar data storage framework of data warehouse, carry out systematic analysis and arrangement, handle (OLAP) so that carry out various analytical approachs such as on-line analysis, data mining (Data Mining), and and then support as decision support system (DSS) (DSS), the foundation of person in charge's infosystem (EIS) etc., aid decision making person can analyze valuable information fast and effectively from mass data, so that formulate decision-making and the change of reply external environment fast, help construction business intelligence (BI).
In general, data warehouse can be by relational database, or the various dimensions database that aims at the data warehouse exploitation sets up, and its framework can be divided into starlike and the flakes framework, comprises several dimension data tables, and a fact table.The subject matter in available data warehouse is: data warehouse lacks the knowledge model that solid theoretical foundation is arranged, and can't rise to the mass data of wherein storage the height of knowledge, therefore also just is difficult to realize final goal---the Knowledge Discovery of data warehouse.
Summary of the invention
The purpose of this invention is to provide a kind of data warehouse model that is used to store multidimensional knowledge, can realize Knowledge Discovery, realize the unlimited association of knowledge and find required knowledge fast, to overcome the prior art above shortcomings.
The objective of the invention is to be achieved through the following technical solutions:
A kind of data warehouse model that is used to store multidimensional knowledge comprises:
Absolute dimension layer, it comprises absolute time dimension module, absolute place dimension module, Main classification dimension module, wherein the time dimension module is divided into year, month, day, hour, min, second a plurality of ranks, place dimension module is divided into a plurality of ranks in country, province, city, district, small towns and street, and Main classification dimension module is divided into some secondary classification dimension modules;
The set layer, it comprises event module, molded tissue block, relationship module, the set layer comprises physical layer by the relative dimension layer of its inside;
Tie up layer relatively, it comprises relative time dimension module, place dimension module, role tie up module relatively, wherein the time dimension module is past, the present and the future, relatively place dimension module be East, West, South, North and in, it is that the position of organizational structure the inside is divided and the Party A Party B of contract the inside that the role ties up module;
Physical layer, it comprises personnel's module, article module, contact method module;
Described absolute dimension layer, set layer, dimension layer and physical layer all are made up of node relatively, and node is the minimum unit in the knowledge model, and it is brightness and state that node has two basic parameters, and wherein how bright brightness have after node is lighted for this reason; Node is bright does not for this reason have for state, state be divided into three kinds promptly dark, illuminate, light;
Described data warehouse also comprises following table: place dimension table, Main classification dimension table, secondary classification dimension table, dimension table, primary entity set table, expansion entity sets table relatively;
The extraction of described data warehouse, conversion, loading procedure need import to raw data in the data warehouse, the field of raw data table can be divided into five classes: the entity identification field, relatively tie up field, set identification field, definitely tie up field and raw data location field, wherein the entity identification field comprises personnel's numbering, Item Number, contact method numbering; The dimension field comprises time dimension, place dimension, role's dimension relatively; The set identification field comprises Case Number, organization number, relation numbering; Absolute dimension field comprises time dimension, longitude dimension, latitude dimension, place dimension, classification dimension; The raw data location field can be one or more fields.
Beneficial effect of the present invention is: hold various dimensions knowledge such as time module, location module, sort module, and multiple set such as events or activities, organizational structure, kinship, message registration, flight, hotel, also have multiple entities such as personnel, article, house property, motor vehicles, contact method, Bank Account Number.Existing social network sites, knowledge website, tracking of information and track such as study and judge at system, can realize Knowledge Discovery, realize that knowledge is unlimited related and find required knowledge fast.
Description of drawings
With reference to the accompanying drawings the present invention is described in further detail below.
Fig. 1 is the described data warehouse model block diagram that is used to store multidimensional knowledge of the embodiment of the invention.
Embodiment
As shown in Figure 1, the described a kind of data warehouse model that is used to store multidimensional knowledge of the embodiment of the invention, comprise: absolute dimension layer, comprise absolute time dimension module, absolute place dimension module, Main classification dimension module, wherein the time dimension module is divided into year, month, day, hour, min, second a plurality of ranks, place dimension module is divided into a plurality of ranks in country, province, city, district, small towns and street, and Main classification dimension module is divided into some secondary classification dimension modules; The set layer comprises event module, molded tissue block, relationship module, and the set layer comprises physical layer by the relative dimension layer of its inside; Tie up layer relatively, comprise that relative time dimension module, relative place dimension module, role tie up module, wherein the time dimension module is past, the present and the future, relatively place dimension module be East, West, South, North and in, it is that the position of organizational structure the inside is divided and the Party A Party B of contract the inside that the role ties up module; Physical layer comprises personnel's module, article module, contact method module, and for example, a people, article, a photo, an E-mail address all are unique, all are entities.
The set layer is the dimension of one and entity for contact, and wherein dimension is positioned at the bottom of knowledge model, and entity is positioned at the top layer of knowledge model.Set occupies regular hour dimension, place peacekeeping Main classification dimension, and comprises one or more entities.A set can comprise one or more entities, an entity also can be subordinated to one or more set, these all are set for organizational structure, events or activities, good friend's circle, interpersonal relation, Relationship of Real Right, set does not directly comprise entity, but comprises entity by gathering inner dimension.The dimension of set outside is called absolute dimension, and the inner dimension of set is called relative dimension, and dimension also is divided into relative time dimension, relative place dimension, classification dimension relatively (perhaps making the role tie up) relatively.Each dimension is made up of a plurality of dimension nodes, and each set is made up of a plurality of collector nodes, and each entity is made up of a plurality of entity nodes.Described absolute dimension layer, set layer, dimension layer and physical layer all are made up of node relatively, and node is the minimum unit in the knowledge model, and it is brightness and state that node has two basic parameters, and wherein how bright brightness have after node is lighted for this reason; Node is bright does not for this reason have for state, state be divided into three kinds promptly dark, illuminate, light; Node is a minimum unit of the present invention, and node has the multiple form of expression, and in the network diagramming of front page layout, node shows as network node; In the data warehouse on backstage, node shows as data; In one piece of article, node shows as in short; In the operation of user to knowledge model, node shows as the knowledge that the user increases.Do not carrying out knowledge when searching, all nodes all are dark; Carrying out knowledge when searching, wherein the sub-fraction node can be lighted, and the node of lighting can illuminate the other node that connects with it again, and the part in these nodes can be lighted again, continuous so outside development will form a rule and continuously search the path.Because the node in the knowledge base is universal relation, so, can both arrive other any one node no matter from which node.
The brightness of all nodes all is same initial value, the brightness that is to say all nodes is all identical, the brightness meeting of node changes under the influence of 3 kinds of factors, 3 kinds of factors are the time, light number of times, evaluation, time is long more, the brightness of node is more little, the more little brightness of brightness to reduce speed slow more, finally can level off to 0; Whenever light once, it is bigger that the brightness of node will become, and gathering way of the big more brightness of brightness is slow more, finally can level off to a high-high brightness; Estimate highly more, brightness is also high more.Brightness change curve and brightness in time stacks up with lighting the number of times change curve, total the brightness change curve that has just formed.Total brightness curve is: being at the beginning the brightness initial value increases then quickly, reaches the brightness maximal value, descends then, and decline rate is more and more slower, levels off to 0 at last; The brightness curve of node, the truth in the world that can reflect reality basically.After a node was lighted, on the one hand, it can illuminate and its direct-connected other node; On the other hand, it can illuminate other node with its close together, and node brightness is high more, it illuminate the distance big more, it is also big more to illuminate scope.
Described data warehouse also comprises following table: place dimension table, Main classification dimension table, secondary classification dimension table, dimension table, primary entity set table, expansion entity sets table relatively;
Place dimension table:
| Field | Data type | Attribute | Explanation |
| Coding | Character string type | Major key | Preceding 2 bit representations preceding 6 bit representations of provincial preceding 4 bit representation city-levels preceding 9 bit representation small towns at county level street |
| Preceding 12 bit representations occupy villagers' committee | |||
| Start Date | The date type | Major key | Time dimension |
| Title | Character string type | ||
| Brightness | Numeric type | ||
| The founder | Character string type | ||
| The icon title | Character string type | ||
| Close Date | The date type | Time dimension | |
| Polygon | Character string type | The polygon CSV of forming by coordinate points |
Main classification dimension table:
| Field | Data type | Attribute | Explanation |
| Coding | Character string type | Major key | 3 grades of 6 bit representations before 2 grades of 4 bit representations before 1 grade of preceding 2 bit representation |
| Start Date | The date type | Major key | Time dimension |
| Title | Character string type | ||
| Brightness | Numeric type | ||
| The founder | Character string type | ||
| The icon title | Character string type | ||
| Close Date | ??date | Time dimension | |
| Other classification dimension 1 implication | Character string type | ||
| Other classification dimension 2 implications | Character string type | ||
| Other classification dimension 3 implications | Character string type | ||
| Other numerical value is tieed up 1 implication | Character string type | ||
| Other numerical value is tieed up 2 implications | Character string type | ||
| Other numerical value is tieed up 3 implications | Character string type |
Secondary classification dimension table:
| Field | Data type | Attribute | Explanation |
| Coding | Character string type | Major key | Preceding 4 bit representations classification |
| Start Date | The date type | Major key | Time dimension |
| Title | Character string type | ||
| Brightness | Numeric type | ||
| The founder | Character string type |
| Field | Data type | Attribute | Explanation |
| The icon title | Character string type | ||
| Close Date | The date type | Time dimension |
Dimension is shown relatively:
| Field | Data type | Attribute | Explanation |
| Coding | Character string type | Major key | |
| Start Date | The date type | Major key | Time dimension |
| Title | Character string type | ||
| Brightness | Numeric type | ||
| The founder | Character string type | ||
| The icon title | Character string type | ||
| Close Date | The date type | Time dimension |
Primary entity set table:
| Field | Data type | Attribute | Explanation |
| Numbering at the corresponding levels | Character string type | Major key | This is the most important sign of this entity or set.For entity,, be exactly citizenship number so if citizenship number is arranged; Were it not for citizenship number and Chinese Name is arranged, is exactly Chinese Name so.For set, if Institution Code in a organized way is exactly organization mechanism code so; Were it not for organization mechanism code and enterprise name is arranged, is exactly enterprise name so. |
| The raw data location field | Character string type | Major key | Form by database numbering _ table numbering _ Major key |
| Brightness | Numeric type | ||
| The founder | Character string type |
| Field | Data type | Attribute | Explanation |
| The Main classification dimension | Character string type | Index | |
| The place dimension | Character string type | Index | The administrative division dimension |
| Time dimension | The date type | Major key | Determine time scale according to the time scale field |
| Time scale | Integer type | During 1=/2=month/3=day/4=/5=branch/6=second | |
| Chinese | Character string type | Index | |
| English name | Character string type | Index | |
| The relative time dimension | Character string type | Index | |
| Tie up in the place relatively | Character string type | Index | |
| The reciprocal role dimension | Character string type | Index | |
| Higher level's numbering | Character string type | Index | Quote " numbering at the corresponding levels " field of this table.For this field of set is empty. |
| Higher level's raw data location field | Character string type | Index | Quote " raw data location field " field of this table.For this field of set is empty. |
| The longitude dimension | The floating number type | ||
| The latitude dimension | The floating number type | ||
| Other classification dimension 1 | Character string type | Index | |
| Other classification dimension 2 | Character string type | Index | |
| Other classification dimension 3 | Character string type | Index | |
| Other numerical value dimension 1 | The floating number type | ||
| Other numerical value dimension 2 | The floating number type | ||
| Other numerical value dimension 3 | The floating number type | ||
| Remarks | Character string type |
Expansion entity sets table:
| Field | Data type | Attribute | Explanation |
| Field name | Character string type | Major key | |
| Numbering at the corresponding levels | Character string type | Major key | |
| The raw data location field | Character string type | Major key | Form by database number table numbering Major key |
| Brightness | Integer type | ||
| The founder | Character string type | ||
| Field type | Character string type | ||
| Literal value | Character string type | ||
| Digital value | The floating number type | ||
| Remarks | Character string type |
The extraction of described data warehouse, conversion, loading procedure need import to raw data in the data warehouse, the field of raw data table can be divided into five classes: the entity identification field, relatively tie up field, set identification field, definitely tie up field and raw data location field, wherein the entity identification field comprises personnel's numbering, Item Number, contact method numbering; The dimension field comprises time dimension, place dimension, role's dimension relatively; The set identification field comprises Case Number, organization number, relation numbering; Absolute dimension field comprises time dimension, longitude dimension, latitude dimension, place dimension, classification dimension; The raw data location field can be one or more fields.
Each table all have at least 1 core field, this core field can be the entity identification field, relatively tie up field, set identification field, definitely tie up field, the core field generally be a sky.
Represent RIA (the rich the Internet application) technology of interface employing, comprise that mainly login page, browsing pages, navigator window, coordinate axis window, set entity launch window, set entity filter window based on Web (internet).Login page is fairly simple, comprises input frames such as user name, password, and is the same with common Web login page.Browsing pages is the homepage of the data in the browsing data warehouse.It adopts monomer RIA, comprises a plurality of identical Shipping Options Pages, can open or close Shipping Options Page, is similar to Web browser.The left side of each Shipping Options Page is a tool bar, and a plurality of buttons are arranged on the tool bar, and button click can eject certain function window, and function window floats at upside and right side.Tool bar comprises: homepage, history, collection, address navigation, search navigation, figure control, filtration, collection, derivation, printing, option etc.The main part of Shipping Options Page is a coordinate system with the multidimensional knowledge in the patterned way video data warehouse, and coordinate system has X-axis, Y-axis and Z axle, shows time dimension, place peacekeeping Main classification dimension respectively.The network that is distributing in the coordinate system and be made up of node and line, node are represented and are gathered and entity, and line is represented dimension relatively, by click tools bar and coordinate system, can realize section, rotation, the upward operations such as brill, following brill of data warehouse.Navigator window is a function window in the browsing pages, function window ejects by the click tools bar, function window is divided into address navigation window and search navigation window, the address navigation window is for navigating according to three dimensions (time dimension, place peacekeeping Main classification dimension), and the search navigation window is to navigate according to keyword.The coordinate axis window is a function window in the browsing pages, and it ejects by double-clicking coordinate axis, and its effect is to carry out the section of dimension and go up brill, following boring, and the rotation of dimension is finished by figure control window.Set entity expansion window is a function window in the browsing pages, and it is to eject by certain set or the entity of double-clicking in the coordinate system.On certain coordinate points of coordinate system, have a plurality of set or entity sometimes, these set and entity can overlap each other, and top meeting covers following.In this case, a plus sige can appear on the set of this point or the icon of entity, click it and will eject set entity expansion window, in window, can show each set and entity clearly, set entity filter window is a function window in the browsing pages, ejects by the click tools bar.
Claims (4)
1. data warehouse model that is used to store multidimensional knowledge is characterized in that it comprises:
Absolute dimension layer, it comprises absolute time dimension module, absolute place dimension module, Main classification dimension module, wherein the time dimension module is divided into year, month, day, hour, min, second a plurality of ranks, place dimension module is divided into a plurality of ranks in country, province, city, district, small towns and street, and Main classification dimension module is divided into some secondary classification dimension modules;
The set layer, it comprises event module, molded tissue block, relationship module, the set layer comprises physical layer by the relative dimension layer of its inside;
Tie up layer relatively, it comprises relative time dimension module, place dimension module, role tie up module relatively, wherein the time dimension module is past, the present and the future, relatively place dimension module be East, West, South, North, in, it is that the position of organizational structure the inside is divided and the Party A Party B of contract the inside that the role ties up module;
Physical layer comprises personnel's module, article module, contact method module.
2. the data warehouse model that is used to store multidimensional knowledge according to claim 1, it is characterized in that: described absolute dimension layer, set layer, relative dimension layer and physical layer all are made up of node, node is the minimum unit in the knowledge model, it is brightness and state that node has two basic parameters, and wherein how bright brightness have after node is lighted for this reason; Node is bright does not for this reason have for state, state be divided into three kinds promptly dark, illuminate, light.
3. the data warehouse model that is used to store multidimensional knowledge according to claim 1 and 2, it is characterized in that described data warehouse also comprises following table: place dimension table, Main classification dimension table, secondary classification dimension table, dimension table, primary entity set table, expansion entity sets table relatively.
4. the data warehouse model that is used to store multidimensional knowledge according to claim 1 and 2, it is characterized in that: the extraction of described data warehouse, conversion, loading procedure need import to raw data in the data warehouse, the field of raw data table can be divided into five classes: the entity identification field, relatively tie up field, set identification field, definitely tie up field and raw data location field, wherein the entity identification field comprises personnel's numbering, Item Number, contact method numbering; The dimension field comprises time dimension, place dimension, role's dimension relatively; The set identification field comprises Case Number, organization number, relation numbering; Absolute dimension field comprises time dimension, longitude dimension, latitude dimension, place dimension, classification dimension; The raw data location field is one or more fields.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105844335A (en) * | 2015-01-15 | 2016-08-10 | 克拉玛依红有软件有限责任公司 | Self-learning method based on 6W knowledge representation |
| CN106126486A (en) * | 2016-06-30 | 2016-11-16 | 童晓冲 | Temporal information coded method, encoded radio search method, coding/decoding method and device |
| CN106326438A (en) * | 2016-08-26 | 2017-01-11 | 南威软件股份有限公司 | Personnel information correlating method |
| CN107688600A (en) * | 2017-07-12 | 2018-02-13 | 百度在线网络技术（北京）有限公司 | Knowledge point method for digging and device |
-
2010
- 2010-07-16 CN CN2010102284137A patent/CN101882164A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105844335A (en) * | 2015-01-15 | 2016-08-10 | 克拉玛依红有软件有限责任公司 | Self-learning method based on 6W knowledge representation |
| CN105844335B (en) * | 2015-01-15 | 2018-04-20 | 克拉玛依红有软件有限责任公司 | A kind of self-learning method based on the 6W representations of knowledge |
| CN106126486A (en) * | 2016-06-30 | 2016-11-16 | 童晓冲 | Temporal information coded method, encoded radio search method, coding/decoding method and device |
| CN106126486B (en) * | 2016-06-30 | 2019-03-08 | 童晓冲 | Temporal information coding method, encoded radio search method, coding/decoding method and device |
| CN106326438A (en) * | 2016-08-26 | 2017-01-11 | 南威软件股份有限公司 | Personnel information correlating method |
| CN106326438B (en) * | 2016-08-26 | 2019-08-13 | 南威软件股份有限公司 | A kind of correlating method of personal information |
| CN107688600A (en) * | 2017-07-12 | 2018-02-13 | 百度在线网络技术（北京）有限公司 | Knowledge point method for digging and device |
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