The research of intelligence data mining Oriented to battlefield Situation Assessment Liu Jing-Xue a, Tang Wei b

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1 Appled Mechancs and Materals Onlne: ISSN: , Vols , pp do: / Trans Tech Publcatons, Swtzerland The research of ntellgence data mnng Orented to battlefeld Stuaton Assessment Lu Jng-Xue a, Tang We b PLA Academy of Natonal Defence Informaton, Wuhan, , Chna. a emal: ljx_62@sohu.com, b emal:wetang1987@sna.com Keywords: battlefeld stuaton assessment; knowledge context; ntellgence data warehouse; data mnng arthmetc. Abstract. Battlefeld stuaton assessment has a postve sgnfcance on mprovng the effcency of commandng decson-makng; moreover, battlefeld stuaton assessment cannot be made successfully wthout the support of some ntegrated and exact ntellgence data. In ths paper, basng on the demand of dentfyng the battlefeld stuaton, the correspondng knowledge context database was frst dscussed; on ths basc, constructon of the ntellgence data warehouse s framework was explored. Then, the study of data mnng based on the ntellgence data warehouse was made from the vew of a holstc concepton, and a detaled arthmetc was presented by makng use of the tactc from data mnng drven fshbone. Introducton Battlefeld stuaton assessment(bsa)s an mportant measure that heghtens the capacty of appercevng battlefeld stuaton, t plays an mportant role n enhancng the effcency of commandng decson-makng. Generally speakng, BSA ncludes the contents of determnng the set of supposed battlefeld stuaton(sbs), dentfyng each SBS and comng nto beng the report of the stuaton analyss etc. However, dentfyng each SBS s the key problem; t can not be made successfully wthout the support of some ntegrated and exact ntellgence data. Thereupon, n ths paper, how to construct the knowledge context database of dentfyng each SBS was frst dscussed; then, from the vew of holstc concepton, the research of data mnng n ntellgence data warehouse s made by usng the drecton of the knowledge context database and the tactc from data mnng drven fshbone (DMDF)[1], and a correspondng arthmetc s brought forward; last, the assessment of battlefeld stuaton s smply studed. The buldng of knowledge context database on dentfyng battlefeld stuaton In general, that dentfes the battlefeld stuaton needs to analyze some supposed battlefeld stuatons, whch come from the elementary analyss of obtaned battlefeld ntellgence data. Durng dentfyng a SBS, t s necessary to explan the SBS by one correspondng evdence chan that s comparatvely ntegrated; moreover, the nformaton n the evdence chan should be emphatcally mned. Consequently, the knowledge context database should nclude the framework nformaton of each evdence chan that s used to dentfy the correspondng SBS. Furthermore, the framework nformaton n each evdence chan should nclude the outlne of an ntegrated story produced by usng stuatonal logc[2] to explan the correspondng SBS. In order to actvate data assocaton, the knowledge context database conssts of the thematc key word datasheet, the sub-thematc key word datasheet and the confgurable key word datasheet. As a rule, one thematc key word corresponds to some sub-thematc key words, and one sub-thematc key word corresponds to some confgurable key words. The knowledge context database leads the demand of battlefeld ntellgence reconnassance as well as drects data mnng. The constructon of ntellgence data warehouse Themes are always the core that organzes data warehouse[3], so the themes should be frst determned so as to load ntellgence data when the ntellgence data warehouse s bult. For the purpose of easly dentfyng battlefeld stuaton, the ntellgence data warehouse should be bult accordng to the themes n the knowledge context database on dentfyng battlefeld stuaton. All rghts reserved. No part of contents of ths paper may be reproduced or transmtted n any form or by any means wthout the wrtten permsson of Trans Tech Publcatons, (ID: , Pennsylvana State Unversty, Unversty Park, USA-11/05/16,01:33:15)

2 Appled Mechancs and Materals Vols Generally, the ntellgence data warehouse conssts of many data sets, each of whch relates to a theme. Each data set conssts of some databases, each of whch s used to store the ntellgence data correspondng to a sub-theme. Each database conssts of a mass of datasheets, whch s used to store the ntellgence data of dfferent profle under the same sub-theme. Usually, one evdence chan ncludes the nformaton nvolved n mult-theme. The data structure of ntellgence datasheet should consder the mutual relatonshp of ntellgence data n addton to be capable of descrbng the characterstc nformaton of ntellgence data[4]. Thus, the ntellgence datasheet should nclude the feld of key word, whch s manly used to store the purpose nformaton. In general, an tem of ntellgence datum contans several key words, whch are abstracted from the correspondng ntellgence contents. Each key word stands for one aspect to whch the ntellgence datum s applcable. If a key word s contaned n two tems of ntellgence data, then the two tems of ntellgence data are called as relatonal, otherwse they are called as non-relatonal. If two key words are contaned n the same ntellgence datum, then they are called as relatonal, otherwse they are called as non-relatonal. Because of key word as a brdge, the ntellgence data that are related each another can be mned from the data warehouse so as to be analyzed holstcally. The data mnng arthmetc DMDF s the data mnng method that s extensble and supports mutual operaton, blockng and restructurng[5]. In the followng dscusson, an arthmetc that combnes knowledge context database wth DMDF s brought forward. A detaled arthmetc Assume that R stands for the data source derved from removng the nvald data n the ntellgence data warehouse and H represents a SBS. Gven that D={d 1, d 2,, d n }represents the set that conssts of the thematc key words needed to dentfy H, D j ={d j1, d j2,, d jk[j] }represents the set that conssts of the sub-thematc key words belongng to d j, D ={ d 1, d 2,, d m[t] } (j=1,2,...,n; t=1,2,...,k[j]) represents the set that conssts of the confgurable key words supportng d (j=1,2,...,n; t=1,2,...,k[j]). For the ntellgence data mnng of dentfyng H, the most basc work s to orderly mne some valuable ntellgence data nvolved n D from R by utlzng preference nformaton and centrc clusterng methods. Not losng unversalty, t s presumed that K ={ d 1, d 2,, d r } s the set of stress key word n D. F =D -K s the set of non-stress key word n D. Suppose that b stands for the estmatve value that wegh the mportance of d (0 b 1; =1, 2,, m[t]), the more mportant d s, the bgger b s. Let b 1 =b 2 = =b r =1. For d F, weghng the mportance of d should consder the quantty of the key words related to d n D. If l denotes the quantty of the key words related to d n D (0 l m[t]; =r+1,r+2,, m[t].), then b l m[] t (=r+1, r+2,, m[t]) (1) So, the weght w that corresponds to d D can be calculated by formula (2) w b ( b b b ) (=1,2,, m[t]) (2) 1 2 m[ t] W =(w 1, w 2, w m[t] ) T s called as the weght vector correspondng tod. Presume that S stands for the data set that ncludes the ntellgence data nvolved n d, then S R. Accordng to D, the detaled steps of the arthmetc are as follow: Step 1: Lettng G=, hypothetcally there s one vrtual deal ntellgence I that ncely contans all key words belonged to D, the characterstc vector of U s defned as ( u, u,, u )=(1,1,,1), and V denotes the determned threshold value(0<v<1). 1 2 n U =

3 1664 Mechancal Engneerng, Industral Electroncs and Informaton Technology Applcatons n Industry Step 2: Arbtrarly takng qs, suppose that D q denotes the set that ncely contans all key words n q. If D D q =, then the characterstc vector of q s the zero vector; otherwse, lettng D D q ={ d, d,, d },where 1 p 1 <p 2 < <p m m[t], the characterstc vector of q s p 1 p 2 q q q q defned as U =( u 1, u 2,, u mt []), where p m p m ). The dstance between q and If m[ t] m[ t] q q = 1 = 1 S( I,q)= w u -u w 1-u ( ) q q u p = 1 p 2 I s defned as q u = =u p m =1, q u =0( p 1, p 2,, (3) S I,q V, then G=G+{ q}. Turn to Step 3. Step 3: S =S -{q}, f S, then turn to Step 2, otherwse turn to Step 4. Step 4: For each element n G, ts order s ranked accordng to the dstance between t and the smaller dstance, the more forward order. If q a G and q b G makes ( a) I, S I,q = S( I,q ), suppose that I a or I b respectvely stands for the nformaton quantty ncluded n q a or q b, and C a or C b respectvely stands for the number of the key words n D related to q a or q b, f (I a, C a )>(I b, C b ), then q a should be ranked n front of q b, f (I a, C a )= (I b, C b ), that ncludes more key words s preferental. Turn to step 5. Step 5: The end of arthmetc amng at d. In above arthmetc, V s actually a control parameter. Especally, lettng w mn =mn{ w =1,2,,m[t]}, when 1-w mn V<1, all ntellgence data whch contan at least one key word n D can be mned from S. Lettng w max =max{ w =1,2,, m[t]}, when 1-w max V<1, all ntellgence data that contan at least one stress key words n K can be mned from S. A calculatve example Presume that d s the tth sub-thematc key word under h thematc key word d j n the knowledge context database. Gven that D ={K a, K b, K d, K f, K g, K, K l } stands for the set of key words supportng d, K ={ K a, K b } s the set of stress key word n D. Suppose that R={r 1, r 2,, r 10 } denotes an ntellgence data set; The key words (KW) and nformaton quantty (IQ)ncluded n r s shown n Table1 (=1,2,...,10). Set the threshold value V=0.80, t s requred that the ntellgence data related to D be mned from R and ther orders be ranked. Table 1. Intellgence Data ID Attr r 1 r 2 r 3 r 4 r 5 r 6 r 7 r 8 r 9 r 10 b KW K a, K c, K f K b, K c, K d, K h K a, K c, K j K e, K f, K g, K j K b, K c, K f K g, K h, K K g, K k K d, K l, K p, K q K f, K j, K l K l, K v, K z IQ As shown n Table 1, the quantty of each non-stress key word n D related to other key word s n D s as follows: l 3 =2, l 4 =3, l 5 =5, l 6 =4, l 7 =2. Accordng to the Eq. 1, the correspondng estmatve values of the key words n D can be calculated as follows: b 1 =b 2 =1, b 3 =2/7, b 4 =3/7, b 5 =5/7, b 6 =4/7,b 7 =2/7. By means of the Eq. 2, the correspondng weght vector of the key words n D can be calculated as W=(0.233,0.233,0.067,0.100,0.167, 0.133,0.067) T. Gven that I stands for a vrtual deal ntellgence that ncely contans all key words n D, by utlzng the Eq. 3, the dstance between r R and I can be calculated as follows: S(I, r 1 )=0.233 (1-1) (1-0) (1-0) (1-1) (1-0) (1-0) (1-0)=0.667; smlarly, S(I, r 2 )=0.700, S(I, r 3 )=0.600, S(I, r 4 )=0.600, S(I, r 5 )=0.667, S(I, r 6 )=0.833, S(I, r 7 )= 0.833, S(I, r 8 )=0.866, S(I, r 9 )=0.700, S(I, r 10 )= the threshold value V=0.80, the ntellgence data set mned from R s G={ r 1, r 2, r 3, r 4, r 5, r 9 }. Accordng to Step 4 n the above arthmetc, the order of ntellgence data n G can be ranked as follows: r 4, r 3, r 5, r 1, r 2, r 9.

4 Appled Mechancs and Materals Vols As shown n Example 1, the arthmetc can effectvely mne the ntellgence data related to D ; t has the characterstcs of easy calculaton and better maneuverablty. Accordng to the requrement of dentfyng H, that orderly arranges the mned ntellgence data nvolved n D (j=1,2,...,n; t=1,2,..., k[j]) can come nto beng an evdence chan. The commander and battle branmen can judge whether to accept H by evaluatng the ntegralty of ths evdence chan and the degree of ths evdence chan supportng or dsprovng H. For a SBS, f the battle branmen can combne the nferable conclusons derved from the correspondng evdence chan and the magnary conclusons derved from stuatonal logc nto one ntegrated story ; moreover, ths story can effectvely explan the SBS and llumnate the falsehood of other SBS; then the SBS wll be affrmed by the commander. Basng on the affrmed SBS, BAS can be made successfully[6]. Concluson For a BSA, It s necessary to obtan the support of effectve and ntegrated ntellgence data. Accordng to the demand of BSA and drectng of knowledge context, ths paper makes a tentatve research on data mnng n the ntellgence data warehouse from the vew of whole concept. Frstly, how to buld the knowledge context database of BSA and how to construct the ntellgence data warehouse are smply dscussed from the angle of easly dentfyng each SBS. Then, an ntellgence data mnng arthmetc based on confgurable key words under sub-theme s put forward by combnng the knowledge context database and DMDF. Accordng to the theme key words n the knowledge context database of BSA, the evdence chan of recognzng a SBS can be easly obtaned by orderly usng the data mnng arthmetc. Consequently, ths arthmetc s wth better pertnence and maneuverablty, whch s valdated by a calculatve example. In order to easly apply the above-mentoned method of ntellgence data mnng n commandng nformaton system, an unform structure needs be desgned so as to drectly vst many knds of data source and an unform method needs be desgned so as to transmt query data and metadata. Acknowledgement In ths paper, the research was sponsored and supported by PLA Academy of Natonal Defence Informaton. References [1] Yun Zhang, We-hua,L Yang Chen. DMDF Appled for Multdmensonal Data Mnng Process[J]. Scence Technology and Engneerng. 2010,10(31): 7816~7821. [2] Heuer, Rchards J. Lmts of Intellgence Analyss[J]. Orbs. 2004, 49(1):75~94. [3] Kuntala P, Chu C -H.H, Raghavan V.V. Desgn and Realzaton of Data Warehouse for Coastal Restoraton[C]. Computer Engneerng and Systems, 2006: 466~470. [4] Shu-rong,Wang Yng Zhao. Research and applcaton based on the data warehouse n the OLAP of the Mult- dmensonal analyss show technology[j]. Electronc Desgn Engneerng. 2012,20(14):18~20. [5] Yun Zhang,We-hua L,Yang Chen.The study of mult-dmensonal data flow of fshbone appled for data mnng.the 7th ACIS Internatonal Conference on Software Engneerng Research,Management and Applcatons ( SERA 2009 ) Hakou,Chna. 2009: 86~91. [6] Jng-xue Lu, Yun-Yao Y. Research for All Around Battlefeld Stuaton Assessment[C]. Informatcs n Control, Automaton and Robotcs. Berln: Sprnger press,2011,(2):569~575.

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