Visual Analysis of Vehicle Voice Navigation with Parallel Coordinates

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1 Visual Analysis of Vehicle Voice Navigation with Parallel Coorinates Miao Wang Nanyang Institute of Technology. Henan. China Corresponing Abstract - In the fiel of vehicle voice navigation, users are intereste in iscovering surrouning environental factors or the relationship between factors that can give rise to potential risks. However, high-iensional pattern ata is one of the vital perforance ata of the vehicle. Currently there is no absolute etho to visualize high-iensional or/an ultivariate ata in inlan vehicle systes. In this paper, we present a parallel coorinates of visual analysis technique, by taking avantage of parallel coorinates an tie series plots. An application of its perforance is valiate on atasets associate with vehicles. Its perforance inclue ata selection, iension transfer, color transfor, plot inter-conversion, iension zoo, iension control of cobination of parallel coorinates, an replace, isplay an appen for tie series plots. Keywors - visual analytics, oularity parallel coorinates, tie series plots, vehicle voice navigation area I. INTRODUCTION The existing propose techniques an tools of visualization of vehicle syste ostly epen on huan perception an experiences to gain insight. Generally, ost of the ean huan to analyzing an perceiving abnoral behaviors an risks on vehicle. In orer to enhance the huan unerstaning an perception of ifferent levels of behaviors an risks of inlan vehicle, visual analytics for vehicle has becoe a fervent research fiel in recent years that attepts to accelerate to unerstan an perceive surrouning environental associate with vehicle in the processing of voice navigation. Furtherore, parallel coorinates of visual analytics can visually escribe the siilarity iensions atasets associate with vehicle voice navigation area, an tie series plots ay be as an auxiliary eans to further proote unerstaning an analysis of the correlations between involving vehicle properties, as well as it can visually epict any of two properties, specifically, with tie as the horizontal axis, It is eonstrate that these easures present a prooting visual unerstaning for user to fin an iportant attribute of vehicles, involving possible accients in a certain perio or stage. Parallel Coorinates[1] have a istinguishe an wiely use technology for high-iensional an ultivariate ata in analysis oain of visualization, that is first coe up with by Inselberg [2], an a ata eleent can be transfore in an N-iensional space into a copact 2-iensional visual representation by a polyline crossing parallel axes of each iension for a ultitue of ultiiensional ata sets. It or its variation has been applie to visualization, ata ining, process control, an so forth, because it can concisely an swith isplay ultiiensional ata. Inselberg et al [3].first put into use parallel coorinates etho to solve the visualization proble, afterwars, soe previous iproveents have well one in regar to parallel coorinates etho, such as curve instea of straight line enhance visual effects[4],ifferent hierarchy parallel coorinates isplaye views[5],etc. Visual analytics [6] is the vibrant an ultiisciplinary research fiel of analytic reasoning facilitate by cobining interactive visualization interfaces for evoting to huan ecision-aking an knowlege iscovery on the basis of very large an coplex ata sets. Its goal of facilitating the user to acquire a qualitative unerstaning of the inforation. A wonerful view of visual analytics istinctly inicates feature or structure within the ata an then can help the user to preferably recognize patterns or etect abnoralities, or others, at the sae tie, without reucing inforation content or interfacing ata in any way. In this work, we propose a ore novel esign, visual analytics ethos of oularity-base parallel coorinates(mbpc) to vehicle behavior an voice navigation area in inlan river that contribute to users accurately get hol of changing tren of inforation in vehicle voice navigation area. Duo to in the process of the vehicle sailing, influence by any environental factors navigable waters(fig 1).Therefore, it is particularly iportant to ascertain the connections between what factor or factors that have a significant ipact on the ship, especially with potentially angerous vehicles which ay occur. This wok focuses on the aspects of vehicle orer, vehicle availability, waterway, hyrology, weather, an its visual analytics. The results suggest that MBPC can be use as an alternative etho for fining connections aong a set of variables associate with vehicle an to enhance to unerstan environental factors relate to the vehicle aroun. DOI /IJSSST.a ISSN: x online, print

2 Figure 1. Vessel Navigation Area Surrounings. The contribution of this stuy an the specific benefits of MBPC are as follows: Cosine-base vehicle Properties Siilarity Multiiensional Scaling(CVPSMS): Apache Spark[7], an eerging in-eory processing fraework, has been facilitate Multiiensional Scaling algorith. oularity-base parallel coorinates (MBPC) integrates parallel coorinates sets with iproving parallel coorinates for expression visualization surrounings of vehicle behavior an voice navigation area. tie series plots atrix isplay that row or colun of 2-variable plots presente by each ata eleent of ajacent points are connecte with line segents. The reainer of this paper is organize as follows. In Section 2 we gives a review of relate work. An overview of our syste is escribe in Section 3.Followe by the ipleentation etails of our techniques in Section 4.Finally, conclusions an suggestions for future work are presente in Section 5. II. RELATED WORKS To convey visual vehicle voice navigation area surrounings, it is typically stuie in line with three steps in the processing of visual analytics: ata collection an pre-processing, visual apping an visual presentation, especially focus on in the last two steps. A. Parallel Coorinates This portion ainly contains two portions of parallel coorinates sets an iproving parallel coorinates. Parallel coorinates is a technique pioneere by Inselberg [2] that has been applie for high iensional analysis probles. Subsequently, it is use to the aspects of ata visualization. B. Siilarity Multiiensional Scaling Soe research stuies on siilarity ultiiensional scaling in visualization has been stuie. Ankerst et al[8] applie Eucliean-base istance with heuristic algoriths to search for siilarity easure an optial iension orer, an prove that arrangeent proble of Eucliean-base istance was NP-coplete. Diension orering algoriths in XvTool [9],users coul anually vary the orer of iensions fro a reconfigurable list of iensions. Wei Peng et al[10] presente iension orering algoriths to search for iniizing the visual clutter. These approaches ainly contributions lie in the orering of axes, an to fin axes layouts in parallel coorinator plots. Yang presente a hierarchical structure by grouping a host of iensions so that reuce the coplexity of the reorering iensions. However, all these ethos lack correlation expression between coorinate s axes, at the sae tie, also unavoiably cause inforation loss, such as soe inforation in the original ata space ay be lost, in one way or another. On the other han, Eucliean-base istance etric units woul be affecte by inex in ifferent scale, it is generally require to be stanarize, while the greater the istance, the greater the ifferences between iniviuals; in our work, we introuce space vector cosine siilarity easure is not affecte by the angle inicator scale, the value of cosine fall in the range [-1,1], the larger the value, the saller the ifference. C. Coorinate Axis Diversification Few approaches have been one for extensions of coorinate axes. Fanea et al[16]. propose parallel glyphs that coorinate axis ha been extene to star glyphs to facilitate coparison of the ata an provie interactive However, its ethos only the visual coparison of non-ajacent ites, at the sae tie, ata set is not to large an not easy to observe for non-professional visual DOI /IJSSST.a ISSN: x online, print

3 users. Mao Lin Huang et al propose a arc-base coorinate plots to ientify the features of network attacks by isplaying visual patterns, because the length of arc is longer than the line segents, the ensity of points isplaye in each axis coul be enlarge in in parallel coorinates plane. However, it lacks soe interaction techniques except for brush an harly figuring out the relationship between attributes in non-ajacent positions. The curves possessing soe statistical property linking ata points on ajacent axes are escribe in literature, it utilize the splatting fraework to etect clusters an reuce visual clutter. These approaches of corresponing extensions of the axes ostly an still focus on the line segents, an apply curves to joint the vertices between the two ajacent axes. In this work, we take full avantage of erit of parallel coorinates sets an ake better generalize parallel coorinates to offset the isavantages they have separately, so that eliver an process ultiiensional ata associate with the vehicle behavior. D. Tie Series Plots Matrix Tie series plots are frequently visualize using line plots, where line segent enote the change of ata points over tie. It consists of a row or colun of 2-variable plots with a coon axis, particularly a tie variable. By efault, each ata eleent of ajacent points are connecte with line segents. Zhao et al.present circular teporal histogras to express tie series so that explore the epenency of oveent behaviors, a tie graph or teporal histogra are propose respectively to cobine ensity aps with tie series isplays. These approach of coloring an shaing of ring segents show aggregate values, where the bigger the nuber of the space copartents an the longer tie series, it ay be ifficult to etect the spatio-teporal istribution of object presence. On the other han, ata associate with the vehicle ay change or not be unifor in structure, each factor ay have special characteristics, an easureents on ifferent variables ay be taken each tie. These irregularities yiel ata that is ifficult to work with. To analyze these ata, we enote the response variables to be as a vertical axis, by efault, an the tie-epenent explanatory variables to be as a horizontal axis. Eventually, we test our approaches in ata sets associate with vehicles an eonstrate that these interactive approaches are effective in revealing the connections between ifferent axes, at the sae tie, easier an eeper to unerstan the results of visualization an analyze vehicle voice navigation area surrounings. III. IMPLEMENTATION DETAILS All visualization algoriths an perforance are ipleente an easure for our stuy with experients on a ThinkCentre esktop with inter(r) core(tm) i CPUs 3.4 GHz an 8 GB Meory. The graphics car use is NVIDIA GeForce GT 620 with 1024M of SDDR 3 Meory. The software environent is base on CentOS with VTK. In the following subsections, ipleentation etails about spark facilitate CSMDS an ore specification on syste interface an interaction for users will be iscusse. A. Cosine-Base Vehicle Properties Siilarity Multiiensional Scaling (CVPSMS). A ifferent iension axes orere in the isplay will reveal ifferent relationships of the ata, an affect the user ata analysis an perceive jugent for visualization of ultiiensional ata. An intuitively efficient of relevant properties an abnoral ata have been foun for users by the favorable orer of iensions. In the our oule part of iproving parallel coorinators, the property iensions axis reorere is hanle, an to reey this shortcoing of current tools for vehicle itself inforation an inlan voice navigation river of relational siilarity between neighboring coorinates axes by Cosine-base etho in the following proceures. Let D be the sets of N-iensional object(as follows(1-4)): Whereby: D { X 1, X 2,..., Xi,..., Xj,... Xn} (1) Xi (( xi1, xi2,..., xik,..., xi) T 1 i N,1 k ) (2) Xj (( xj1, xj2,..., xjk,..., xj) T 1 j N,1 k ) (3) where is the nuber of Xi or Xj ata ites, an N is the iension of the ata,an xik or xjk is enote as k -th property value in i -th or j -th respectively. si( ij) is presente with relational between i -th an j -th to eterine the siilarity conition: si ij cos( Xi, Xj ) k1 Xi Xj Xi Xj k1 2 ( xik) ( xikxjk) The larger the value of si( ij) k1 ( x ) 2 jk (4), the higher the DOI /IJSSST.a ISSN: x online, print

4 siilarity between axis. Usually, the ost siilarity between Xi or Xj escribes close to istribution trens in these two properties axes in vehicles ulti-iension ata sets. IV. SPARK FACILITATED CVPSMD To eal with large aounts of oveent ata, soe spatio-teporal ata can be one within a ata warehouse or a atabase, spatio-teporal ata here are loae in RAM for visualization an interactive analysis. Borg propose a funaental theory an applications of oern ultiiensional scaling that is a eans of visualizing the level of siilarity of iniviual cases of a ata sets. Ingra et al. presente a ulti-level ultiiensional scaling algorith base on a parallel force-base subsyste siulation to exploit graphics processing unit (GPU) harware while the GPU parallelis iprove spee of coputation. However, these approaches either apply conventional ata rea oe, or loa the entire ata sets,it ay suffers fro extreely serious coputational an tie costs. In our work, CVPSMD is ipleente by Apache Spark, an eerging in-eory processing fraework, to facilitate siilarity ultiiensional scaling. The approach we present in this work is to a or subtract iension into cosine siilarity ultiiensional scaling configuration, where is firstly constructe an initialize. Seconly, when copleting initialize cosine iension orering configuration, CVPSMD etho is introuce into spark to facilitate iension orering. Next, the users can rag axes into the plot, soe new iensions are ae or subtracte to calculate siilarity aong ifferent neighboring axes in the visualization process of parallel coorinators. TABLE I. PERFORMANCE COMPARISON OF CPU-BASED/GPU-BASED/SPARK-BASED. Coparing the perforance here CPU-base an GPU-base with MS an Siilarity MS(Si-MS),an Spark-base with MS, Eucliean MS an Si-MS to facilitate ultiiensional scaling. By testing visual approach with two atasets of car an vehicle with ifferent sizes, an copare the an with our observation, as showe in table 1,Spark-base Si-MS iproves the perforance over previous other ethos, especially, face with when the ata iensions is progressively increasing. In our ipleentation, the ost coon situation is that the user a iensions aong axes into the plots step by step, the results isplay that Spark-base Si-MS coes observably about coputing iproveents. The velocity of tie cost changes fro 823s to 93s an fro 9284s to 604s by CPU-SiMS an Spark SiMS respectively, relying on the ata iensions with size. Spark facilitate CVPSMD can offer about 10 ties iproveent. A. Representing Vehicle Properties Coorinate Axis Diversification Few approaches have been one for extensions of coorinate axes. Soe of techniques aopt in the for of a single extension of axis or the strategy of apping each single ata into a coorinate axis isplay on the screen. We eploy the ixture of three ifferent coorinate axis that can be ajuste accoring to specific requireents. In this paper,we present a novel coorinates axes, which consist of iversification to exten representation of the coorinate axes in integrate space of parallel coorinates visualization. We shoul touch on, that the frequency-base visualization etho for contribution rate of each axis attribute to ynaic ata an voice navigation ata. The first type of axis is base on user-efine stanarization (USVA) have been propose. The axis can be stanarize as the unifor fors an esigne base on user-efine requireent, as follows (6), v' L' v in( xi) L L' ax( xi) in( xi) (6) Where ax( xi) an in( xi) are the axiu an iniu values for each axis of the original ata respectively, v is the value of a axis, L an L' are the axiu an iniu values for each axis after stanarization respectively, usually let L' is as 0 or user-efine, let L as 1 or user-efine, suggest as a positive integer. v' enotes corresponing to v values for each axis after stanarization. The secon types of axis as bar-base(bba) have been presente. the axis was partitione into k sub-section by the bars, l enotes the length of DOI /IJSSST.a ISSN: x online, print

5 coorinate axis of i th attribute axis, enotes the total nuber of ata ites of i th attribute axis. Each bar has height: H ( k ) Nu( ki) i / where Nu( ki) enotes the nuber of ata ites of i th sub-section in the k section. Each bar has withw ( ki) ; Step1: if Step2: p-1 an if: pq,then W ki) pq p 1, p, p 1, p then W ( ki) p 1, q Step3:q+1 an if: pq,go to step3; q, q 1, p 1, q q, q 1 (,go to step2; then W ( ki) p 1, q 1,go to step2; Step4: if pq,then W ( ki) pq... 1, l is a reference value an a axiu with value, pq is istance between any two ajacent points of i th attribute axis, the sets of pq as {, 12, 23,... pq,... 1 }. These bars ay be viewe when the ouse hover the corresponing to axis, eanwhile, the size of ata associate with bar will also be showe, furtherore, when ouble-click sub-section bar, corresponing to ata ites can be higher highlight in the isplay. For each bar, the count of nuber has been copute fro the counts containe in the bar. The thirly types of axis as layer-base (LBA)have been propose, which was ivie into several section accoring to specific requireents, it siilar to Parallel Sets propose by Robert Kosara.,such as concluing three sub-section of Wuhan city, Huanggang city, Ezhou city. Nevertheless, layer-base axis can support nuerical ata statistical classification, but not in the literature. The three ifferent types of axis are sealessly integrate an can be ajuste by soe interactive techniques accoring to user-controlle requireents. Parallel coorinates are isplaye by controlling file enu button. We see the purple cures that are error value in the atasets of vehicle, not outliers. Statistical classification of ata are processe, the colors are ranoly ae in the BBA. Users can choose their own color in the LBA or USVA, an can also for a variety of perforance operation, such as iension zoo, iension control, etc. Brushing in after an operation, such as brush in re, the ouse is place on point on the axis. B. Representing any Two Involving Vehicle Properties Tie series are special in that tie points where the patterns of ata have a relatively fixe orer. Along with the interactive visualization interface, our syste provies teporal-base views that cobine with other properties, such as vehicle spee, water flow, interacting with parallel coorinates.as shown in the low right part figure2. Tie series plots atrix is isplaye by controlling file enu button. Note, to reuce clutter point of tie for the rouning process. For exaple, 13:20 becoes 13. Figure 4. Properties Coorinate Axis Diversification. DOI /IJSSST.a ISSN: x online, print

6 The line graphical visualization allow users to overlay ifferent features by using check box provie against tie feature for easy coparison an to observe visual trens. Users can select on one or quite a few accient cases hanle by Yangtze River police stations an coprehen inforation at the tie of the accient, such as win power. The nuber of accient cases can be easily observe the peaks in the suer onths for the ile reaches of Yangtze River, enabling users to etect the seasonal trens of their ata sets an giving the a reference inforation an helpful precautions in their own voyage. The tie series isplay inicates ostly regular character of the oveent of the vehicles. The variation of the vehicle presence can be analyze an explaine by voice navigation conition, such as weather conitions. This syste also allows users to weekay an weekly trens of accient cases, an further gives assistance for users to acquire an ore effective vehicle behavior schee. [6] Daniel Kei, Gennay Anrienko, Jean-Daniel Fekete, Carsten Gorg, Jorn Kohlhaer, Guy Melanc. Visual Analytics: Definition, Process, an Challenges. Inforation Visualization, 2008: [7] Zaharia M, Chowhury M, Franklin M, Shenker S, Stoica I. Spark: Cluster coputing with working sets. HotClou [8] M. Ankerst, S. Berchtol, an D.A. Kei. Siilarity clustering of iensions for an enhance visualization of ultiiensional ata. Proc. IEEE Syposiu on Inforation Visualization, pages 52 60,1998. [9] Xvtool hoe page. xv/. xv. [10] Wei Peng, Matthew O. War, Elke A. Runensteiner. Clutter Reuction in Multi-Diensional Data Visualization Using Diension Reorering. IEEE Syposiu on Inforation Visualization, 2004 (15) V. CONCLUSIONS AND FUTURE WORK In this paper, we present a oularity-base parallel coorinates of visual analytics technique, by taking avantage of parallel coorinates an tie series plots. Its perforance eonstrates that oularity-base parallel coorinates are not only closely relate each other but also sealessly integrate together. These easures provie a prooting visual unerstaning for user to fin an iportant attribute of vehicles, involving possible accients in a certain perio or stage. In the future, we will consier the factors of the vehicle pilot or vehicle traffic anageent that are ae to future work, ue to incoplete ata. Siultaneously, we will also consier huan factors, an its vehicle visual analytics, in orer to further facilitate accurate visualization of the vehicle. ACKNOWLEDGMENT This paper is fune by Anyang Science an Soft Science Project, Project Nuber RKX12. REFERENCES [1] INSELBERG A., DIMSDALE B.: Parallel coorinates: a tool for visualizing ulti-iensional geoetry. In Proc. of IEEE Visualization (1990), pp [2] A. Inselberg. The plane with parallel coorinates. The Visual Coputer, 1985,1(2): [3] INSELBERG A.,DIMSDALE B. Parallel coorinates [A], Huan-Machine Interactive syste [C]. Berlin: Springer, 1991: [4] GRAHAM M.,KENNEDY J. Using curves enhance parallel coorinate visualizations[a].proceeings the seventh International Conference on Inforation Visualization [C].Washington DC:IEEE Coputer Society,2003, [5] FUA Y H.,WARD M O.,RUNDENTEINER E A. Hierarchical parallel coorinates for exploration of large atesets [A].Proceeings of the Conference on Visualization 99:Celebrating Ten Years[C]. Los Alaintos: IEEE Coputes Society Press, DOI /IJSSST.a ISSN: x online, print