Improving the Performance of Web Service Recommenders Using Semantic Similarity


 Erick Wilkins
 2 years ago
 Views:
Transcription
1 Improving the Performance of Web Service Recommender Uing Semantic Similarity Juan Manuel AdánCoello, Carlo Miguel Tobar, Yang Yuming Faculdade de Engenharia de Computação, Pontifícia Univeridade Católica de Campina (PUCCampina) Campina, SP, Brail Abtract: Thi paper addree iue related to recommending Semantic Web Service (SWS) uing collaborative filtering (CF). The focu i on reducing the problem ariing from data parity, one of the main difficultie for CF algorithm. Two CF algorithm are preented and dicued: a memorybaed algorithm, uing the knn method, and a modelbaed algorithm, uing the kmean method. In both algorithm, imilarity between uer i computed uing the Pearon Correlation Coefficient (PCC). One of the limitation of uing the PCC in thi context i that in thoe intance where uer have not rated item in common it i not poible to compute their imilarity. In addition, when the number of common item that were rated i low, the reliability of the computed imilarity degree may alo be low. To overcome thee limitation, the preented algorithm compute the imilarity between two uer taking into account ervice that both uer acceed and alo emantically imilar ervice. Likewie, to predict the rating for a not yet acceed target ervice, the algorithm conider the rating that neighbor uer aigned to the target ervice, a i normally the cae, while alo conidering the rating aigned to ervice that are emantically imilar to the target ervice. The experiment decribed in the paper how that thi approach ha a ignificantly poitive impact on prediction accuracy, particularly when the ueritem matrix i pare. Keyword: Collaborative filtering, Recommender ytem, Semantic imilarity, Semantic Web Service, Spare data. 1. INTRODUCTION ServiceOriented Computing (SOC) i a new computing paradigm that ue ervice a building block to accelerate the development of ditributed application in heterogeneou computer environment. SOC promie a world of cooperating ervice where application component are combined with little effort into a network of looely coupled ervice for creating flexible and dynamic buine procee that can pread over many organization and computing platform [1] Among the key challenge for the effective ue of Web ervice i the dicovery of ervice that meet the functional and nonfunctional requirement of it uer and that take into account their preference [2]. In Web ervice dicovery ytem, three entitie can typically be ditinguihed: the ervice requeter (a uer or a program), the ervice provider and the ervice regitry. Entitie eeking ervice make ervice requet to the regitry. In the regitry, the decription of the ervice requeted i compared with the decription of ervice advertied by ervice provider, uing a matching algorithm, to identify whether there are ervice that meet the requet. If the matching i ucceful, the regitry provide the decription of identified ervice intance to the requeter, including the neceary detail for their invocation. Architecture for ervice dicovery, uually baed on the WSDL pecification [3], have eriou limitation ariing from the ervice decription technology and matching algorithm ued. Thee limitation are due, in part, to the ue of informal decription of ervice functionality and capability, written in natural language, uually lacking a common vocabulary for the ervice requeter and provider. Semantic Web Service (SWS) and Linked Service are recent approache that try to overcome thee limitation by combining Web ervice technology with element of the Semantic Web [4][5]. In SWS dicovery architecture, advertied ervice are decribed uing ervice annotation ontologie in addition to WSDL parameter and operation name. Thee ontologie define a emantic model for the decription of a Web ervice from everal perpective, including functionality, execution flow and invocation detail. They define a et of attribute for ervice capability decription, the mot common being the ocalled IOPE (Input, Output, Precondition and Effect). Service annotation, in accordance with a ervice annotation ontology, ue concept contained in domain ontologie intead of nontandardized word, which are more commonly ued in conventional nonemantic approache. Domain ontologie decribe the terminology and the relationhip between term of a pecific domain uing an ontology language uch a OWL or RDFS [6][7]. Each ontology language ha it own unique expreive power, but all can model, at the minimum, hierarchie of concept and role of concept, uch a propertie, attribute and relationhip. When performing a earch, the characteritic of the deired ervice, uch a input and output, are pecified by term that repreent ontology concept. Matchmaking algorithm baed on logical inference can then eek matche for the requet parameter, taking into account the parameter of the available ervice. For each match found, a value that characterize the matching degree (imilarity) i computed. Finally, the identified ervice are returned to the requeter in decending order of matching degree. Search algorithm for emantic Web ervice preent good reult when the uer i able to adequately decribe the deired ervice. However, thi i not alway the cae, and a requet for a ervice cannot correpond fully to the intention of the requeter. For example, there may be a publihed ervice that partially matche the requet and accomplihe the intention of the requeter, or the oppoite cenario could alo conceivably occur [8]. A the number of available ervice on the Web increae, thi problem woren. Currently, a pointed out in [9], one of the mot challenging iue in Web ervice proviion i not the matchmaking proce but the election of good ervice for a target uer. 80
2 In addition, a the number of available Web ervice grow, there may be a lot of intereting available ervice that uer are not aware of, and that they therefore will not take the initiative to requet. Additionally, in the context of mobile and ubiquitou computing, it i unreaonable to aume that a uer i contantly earching for intereting ervice available at the uer locale. In thi context, it i deirable to have a recommender ytem capable of identifying and of proactively recommending potentially intereting ervice to the uer in the right ituation. A web ervice recommender can alo be very valuable to proactively deal with failure and to recover to ervice workflow that have partially failed and in dynamic compoition cenario, provided the ervice and the recommender can deal with emantic markup [10] [11]. The recommendation problem can be reduced to an iue of etimating rating for item that have not been ued before by a uer; item with higher etimated rating are a a conequence recommended to the uer. Recommender are uually implemented uing filtering algorithm claified into three main categorie, depending on how the recommendation are performed: (1) ContentBaed algorithm (CB) filter and recommend item that are imilar to other the uer ha acceed in the pat; (2) Collaborative Filtering (CF) algorithm filter and recommend item baed on the preference of other uer with imilar tate and preference; knowledgebaed (KB) recommender ue knowledge about uer and item to generate a recommendation. It i alo frequent to find hybrid ytem that combine method taken from two or more of the previou categorie of recommender [12] [13]. Contentbaed recommender have their root in the information retrieval field and were uccefully implemented in domain where the item to be recommended are decribed through textual information. Thee ytem are, however, limited by the feature that are explicitly aociated with the item. They are alo limited to recommended item that are imilar to thoe already rated by the uer (over pecialization). A particularly difficult tak for thi type of algorithm i to deal with new uer, becaue new uer have to rate a ufficient number of item before the ytem can undertand their preference and tart making ueful recommendation. CF algorithm do not have ome of the abovementioned hortcoming of content baed algorithm. Since they employ the uer' rating, they can deal with any kind of content and recommend any type of item, even item that are diimilar to thoe acceed in the pat. However CF ytem have their own challenge, including coping with pare data and caling with increaing number of uer and item. Several tructural difficultie related to pare data may be encountered, including the cold tart problem, the reduced coverage problem and the neighbor tranitivity problem. The cold tart problem occur when new uer or item are inerted into the ytem. New item cannot be recommended until they are rated by ome uer, and, in turn, new uer are unlikely to receive good recommendation becaue they lack a rating hitory. The reduced coverage problem occur when the number of rating i very mall compared with the number of item in the rating databae. In thi ituation, the ytem may be unable to generate recommendation for uch uer. The neighbor tranitivity problem occur when uer with imilar tate do not have rated item in common and thu cannot be identified a imilar. Knowledgebaed recommender ytem avoid ome of the drawback of content and CF ytem ince their recommendation do not depend on a bae of uer rating. Their main drawback i the well known knowledge acquiition bottleneck. Algorithm for CF, the primary focu of thi paper, can be further claified into two main categorie: memorybaed and modelbaed. Memorybaed algorithm contruct a neighborhood of uer who have imilar rating to the target uer uing directly the available data. In thi circumtance, the rating of neighbor are ued to predict how a target uer will rate an item he ha not yet acceed. Modelbaed technique employ available rating data to learn a model to make prediction, uually uing data mining or a machine learning algorithm. Then the model i ued to make prediction for target item, intead of uing raw rating data, a i done with memorybaed algorithm. When comparing memorybaed and modelbaed CF algorithm it i uually accepted that memorybaed algorithm are eay to implement and have higher prediction accuracy, particularly for dene dataet. Modelbaed algorithm are, in turn, more calable and le vulnerable to profile injection attack [12]. In the recent pat, recommender ytem have been built for recommending different type of item in divere domain, including CD, Web page, book, new, movie and coure. However, reearch on Web ervice recommendation i in it preliminary tage and uually focue on predicting ervice QoS (Quality of Service) parameter [14], which i a very limited way of capturing uer interet [15]. In thi paper, we preent algorithm for contructing Web ervice recommender ytem aimed at reducing the problem ariing from pare data. The propoed approach combine CF algorithm with logical inference to determine the emantic imilarity between ervice, and between uer. The rationale behind thi approach i that if two uer have not rated a common et of ervice but have rated imilar ervice, thee rating can till be an indication of uer imilarity and therefore contribute to reduce the effect of data parene. The remainder of the paper i organized a follow: ection 2 preent memory and modelbaed CF algorithm for Semantic Web ervice recommendation; ection 3 dicue the experimental et up ued to evaluate the algorithm and the reult that were obtained; ection 4 preent related work; and, finally, ection 5 conclude the paper by pointing out our main reult and direction for future work. 2. CF ALGORITHMS FOR SEMANTIC WEB SERVICE RECOMMENDATION In thi ection, variation of two recommender algorithm that exploit emantic imilaritie among web ervice are preented. Their performance will be compared in Section 3. Intance of uer feedback 1 are tored in a ueritem matrix, repreented a a et T U S F, where U 1 In thi paper we ue the term feedback, core and rating a ynonym. 81
3 = {u 1, u 2,, u m } i the et off all uer, S = { 1, 2,, n } i the et of all rated ervice, F = {f 1, f 2,, f m } i the et of intance of feedback related to ervice in S and collected from the uer in U. Each f u F i an n dimenional vector over the pace of all intance of uer feedback, i.e, f u =( f 1, f 2,, f n ) where f j [0..1] i the feedback given by uer u to ervice j. If a ervice wa not rated it feedback i repreented a φ (null). Although the collaborative filtering algorithm decribed in thi ection are independent of the notation ued to decribe ervice emantic, when they allow for the meaurement of the level of emantic imilarity among two ervice, a prototype for ervice decribed uing OWLS wa implemented for the validation of the algorithm. OWLS i an upper ontology that pecifie that a ervice can be decribed by at mot one ervice model, and a grounding mut be aociated with exactly one ervice [16]. OWLS i a W3C recommendation baed on the W3C tandard OWL, an ontology language for the Semantic Web with formally defined meaning [6]. Computing Service Similarity In our prototype implementation, the degree of imilarity between OWLS ervice i computed uing a hybrid emantic ervice matching algorithm decribed in [17] that take advantage of both logicbaed reaoning and IR technique. If R repreent a requet for a ervice and S a ervice regitered in the ervice databae, the emantic matching algorithm compute the following matching degree: Exact match (S exactly matche R)  The I/O (Input/Output) ignature of S perfectly matche requet R with repect to the logicbaed equivalence of their formal emantic. Plugin match (S plug into R)  All input parameter concept of S match more pecific one in R. In addition, S i expected to return more pecific output data. Subumed match (R ubume S)  Thi matching degree i weaker than plugin matching. The output of S i more pecific than requeted by R a before, but the contraint of immediate output concept ubumption i relaxed to arbitrary output concept ubumption. Subumedby match (R i ubumed by S)  The output of S i lightly more general than requeted (direct parent output concept). Nearetneighbor match (S i the nearet neighbor of R)  It i checked if the degree of text imilarity, SynSim(S,R), between the input and output concept of S and R i greater than or equal to a defined yntactic imilarity threhold α. Thi degree i computed a the averaged yntactic imilarity of the erialized input and output concept of S and R, according to a given imilarity metric. A et of concept i erialized by mean of their expanion through the ontology implemented and by the conjunctive concatenation of the reult into one untructured text document, including only logical operator and primitive component of the baic vocabulary that i preent in the ontological terminology. In the cae of vectorpacebaed text imilarity meaurement, thee document are repreented a weighted keyword vector baed on a termweighting cheme. Fail (S doe not match with R)  None of the above matching degree wa obtained. Memorybaed Feedback Prediction with KNN Thi recommendation algorithm i baed on the contruction of neighborhood of imilar uer. The neighbor rating can then be ued to make prediction for unrated item. A neighborhood i contructed comparing the imilarity of each pair of exiting uer uing the Pearon Correlation Coefficient (PCC). Two variant of the algorithm were implemented. In the firt, named PCC, the imilarity between two uer u and v, im(v), i computed a hown in Eq. (1), where S uv = { f φ and f v, φ } i the et of ervice that both uer, u and v, have rated, f [0..1] i the feedback given by uer u to ervice and f u and f v are the average of the intance of feedback given by uer u and v, repectively. im(v)= S uv S uv ( f ( f f u)( fv, f v) (1) f u) 2 Su v ( f v, f v) In Eq. (1), if uer u and v have not rated item in common it i not poible to compute their imilarity. Alo, if the number of common item that were rated i very low, the computed imilarity may be unreliable. In the econd variant of the algorithm, named PCC SS (PCC with imilar ervice), it i not required that uer u and v rate the ame ervice to compute their imilarity a it take into conideration the rating of imilar ervice. The imilarity between ervice i computed uing the emantic matching algorithm preented in the previou ubection. PCCSS compute the imilarity between two uer, u and v, uing Eq. (2). In that equation, t i the ervice rated by v that i mot imilar to (rated by u), repecting a minimum threhold of imilarity δ. When both uer have rated the ame ervice, and t repreent the ame ervice (the imilarity between and t i 1). im(v)= u S ( f f u)( fv, t f v), (2) Su t Sv ( f f ) 2 u t Sv ( f v, t 2 f v) The imilarity between two uer, im(v), computed uing Eq. (1) or Eq. (2), range from 1 to 1. A value of 1 implie a line that decribe the relationhip between feedback f and f v, given from uer u and v, repectively, for ervice (or a imilar ervice), with all data point (intance of feedback) lying on the line where f v, increae a f increae. A value of 1 implie that all data point lie on the line where f v, decreae a f increae. A value of 0 implie that there i no linear correlation between the variou intance of feedback. In our implementation only im(v) value higher than 0 were conidered relevant. The feedback a uer u would give to a ervice that he ha not yet rated can be etimated uing the rating that neighbor uer aigned to that ervice. Having a neighborhood V, the feedback uer u would give to ervice, f, can be predicted uing two variant of the weighted average of all neighbor rating, a hown in Eq. (3) and Eq. (4). 2 82
4 im( v)( fv, f v) V f = f u + v (3) im( v) v V For the reult f in Eq. (3), hereafter named WAAR (Weighted Average of All Rating), the neighborhood V i formed by the k mot imilar uer to u that rated ervice. im( v)( fv, t f v) v V fu = f u + (4), im( v) v V For the reult f in Eq. (4), hereafter named WAAR SS (Weighted Average of All Rating with Service Similarity), the neighborhood V i formed by the k mot imilar uer to u that rated ervice or a ervice t that i emantically imilar to. If V or V i empty, repectively in Eq. (3) or (4), f i made equal to f u. Modelbaed Feedback Prediction with Kmean Memorybaed filtering algorithm tend to be more accurate than modelbaed algorithm, but the latter are more calable and le vulnerable to profile injection attack [18]. Conidering that the number of available ervice in the Web i continuouly increaing, and that in the context of Webbaed open collaborative recommender the likelihood of attack i not negligible, modelbaed recommender algorithm can be good alternative to memorybaed algorithm, provided that their accuracy i acceptable We decribe in thi ection a modelbaed CF algorithm for emantic Web ervice that ue the kmean clutering method and the concept of emantic ervice imilarity. The kmean method i ued to partition a et of point or obervation into cluter. If we conider that f u F define the profile of uer where f u i the vector of intance of feedback given by uer u for the available ervice, the kmean algorithm can be ued to cluter uer with imilar profile. Once the cluter are defined, their centroid can be interpreted a aggregated profile of the uer in the cluter a done in [19]. The clutering algorithm work a follow. Initially k point (f vector) are randomly choen a the initial cluter centroid, after which an aignment tep and an update tep are repeated until the algorithm converge. In the aignment tep, each point i aigned to the cluter with the cloet centroid. In the update tep, cluter centroid are updated to the mean of the point aigned to the cluter. The algorithm converge when the centroid no longer change. In the aignment tep, the ditance between a point and a cluter centroid i computed uing the PCC or the PCCSS (Eq. 1 and Eq. 2, repectively). Following the aignment tep, the update tep compute a new centroid f c =( f c,1, f c,2,, f c,n ) for each cluter c. The new centroid vector i the mean of the uer profile aigned to cluter c. That i, f c,i, for i = 1 to n, i computed by Eq. (5). 1 = fu i (5) c f c, i, u C When applying Eq. (5), if ome f i i equal to φ (meaning that uer u ha not rated ervice i,), the average core of the item rated by u i intead ued. When the algorithm converge, each cluter centroid i een a an aggregation of the uer profile in their repective cluter. Uer intance of feedback for unrated ervice are then etimated uing Eq. (3) or Eq. (4), taking into conideration the neighborhood formed by the k cluter (repreented by their centroid) mot imilar to the target uer profile (repreented by hi feedback vector). 3. EXPERIMENTAL EVALUATION The purpoe of thi ection i to compare the performance of the algorithm preented on ection 2. The lack of public rating dataet i a major difficulty when validating recommender ytem for Web ervice. To circumvent thi difficulty, reearcher uually adapt popular dataet contructed to recommend other type of item. For example, [20] ue the Movielen 2 dataet and conider that a movie in the dataet repreent a Web ervice. The evaluation of the algorithm we propoe, add an additional level of difficulty becaue we need a dataet of uer rating for emantic Web ervice. In thi context, an alternative i to yntheize a dataet that matche the propertie of the target domain and tak [21]. Following thi approach we created a ynthetic ueritem matrix that can be ued to provide ome inight into the behavior of the implemented algorithm and erve a a proof of concept. We ued ervice from the OWLS Service Retrieval Tet Collection  OWLSTC 3, verion 2.2, a collection of 1004 Web ervice from everal domain, pecified according to the OWLS ontology. In the experiment, two group with 50 uer each were defined. Each uer rated 56 ervice from the following four categorie: car, camera, hotel and urf. Service rating were et according to a bae feedback defined for each pair (uer_group, ervice_category). Each feedback wa added to a value that varie from 1 to 1 according to the normal ditribution. The main objective of the experiment wa to analyze the behavior of the propoed algorithm conidering dene and pare data cenario. Thee cenario were imulated by progreively hiding a number of ervice rating from the algorithm: the 56 ervice rating for each uer were progreively reduced in tep of 10 until only 6 rating were available for each uer. After each removal tep, the value of the removed core were etimated uing the algorithm previouly dicued, with and without taking into conideration imilar ervice, following which the average error of the prediction wa computed. The experiment for each removal cenario were repeated 10 time and the reult averaged. The time needed to compute the imilaritie between ervice wa not taken into conideration becaue the computation were performed before running the experiment. The prediction performance of the algorithm wa meaured uing the Mean Abolute Error (MAE) and the Normalized Mean Abolute Error (NMAE), defined by Eq. (6) and Eq. (7), repectively
5 MAE = p N f MAE (7) NMAE = f N In Eq. (6), p denote the predicted feedback that uer u will give to ervice, f denote the actual (hidden) feedback that uer u gave to ervice, and N i the number of predicted intance of feedback. Lower value for MAE and NMAE indicate better prediction quality. A MAE or NMAE equal to zero correpond to an ideal cenario, where all prediction are equal to the actual intance of feedback. Evaluating the KNN Memorybaed Feedback Prediction Algorithm In the experiment decribed in thi ection two ervice are conidered imilar if their matching degree i Exact, Plugin, Subume, Subumedby or Nearetneighbor with a threhold α of 0.8. Two imple etimation cheme, the itemmean and the uermean algorithm, were alo implemented to be ued a baeline. The itemmean (IMEAN) algorithm etimate the core for an item (a ervice) a the mean of the core the target item received from all uer that rated it. The uermean (UMEAN) algorithm etimate the core for an item a the mean of the core the target uer gave to the item he rated. When applying Eq. (3) or Eq. (4) (WAAR and WAARSS), the neighborhood ued to etimate a core i defined by uer with a degree of imilarity to the target uer that i greater than or equal to 0.8, a computed by Eq. (1) or Eq. (2) (PCC and PCCSS). When etting thi imilarity threhold, we have to conider that if it i too low uer with low imilarity can be conidered neighbor, negatively affecting the accuracy of the algorithm. On the other hand, if the threhold i very high it i poible that no neighbor will be found, making it impoible to predict feedback from the target uerervice pair. A can be oberved in Figure 1, the prediction error when uing the PCC and WAAR (without uing ervice imilarity) i ignificantly lower than when the IMEAN and UMEAN algorithm are ued. In other word, NMAE (6) IMEAN UMEAN KNN without ervice imilarity 0.00 Number of rating removed Figure 1. Prediction accuracy of IMEAN, UMEAN and knn without ervice imilarity NMAE Without ervice imilarity WAAR w ith ervice imilarity 0.00 Number of rating removed PCC with ervice imilarity PCC and WAAR w ith ervice imilarity Figure 2. Impact of ervice imilarity on the accuracy of the KNNbaed prediction algorithm conidering a neighborhood of imilar uer to predict uer feedback i better than uing raw uer or item average. Figure 2 how that conidering ervice imilarity increae the prediction performance to an even greater extent. Thi happen when ervice imilarity i ued only to compute the PCCSS (Eq. (2)) for the purpoe of finding a neighborhood, or to etimate core with WAARSS (Eq. (4)). Uing ervice imilarity both to compute the PCCSS and the WAARSS produce even more accurate prediction. Thee reult can be explained a follow. When the PCC i computed without taking into conideration ervice imilarity, everal imilar uer are not identified becaue the PCC equation correlate only uer that rated a common et of ervice. When ervice imilarity i taken into account, uer who rated imilar ervice are alo taken into conideration, increaing the neighborhood and, a a conequence, the accuracy of the algorithm. In addition, uing ervice imilarity to predict a rating (WAARSS) contribute to increae the accuracy becaue it allow more core to be conidered when calculating the prediction. Thi happen becaue intead of only conidering ervice core that the target uer and their imilar uer rated, core for imilar ervice are alo included. Figure 2 alo how that the effect of conidering ervice imilarity are not ignificant when a mall amount of core i removed, but are more dramatic when the amount of removed core increae, that i, when the ueritem matrix become parer. A hown in figure 2, when 50 out of 56 core are removed, the NMAE i equal to 0.23 if ervice imilarity i conidered in both the PCC and WAAR, while when it i not conidered in any of the method it rie to 0.41, an increae of 78%. Evaluating The Kmean Modelbaed Feedback Prediction Algorithm Uing the ame cenario from the previou ection, experiment were conducted to evaluate the performance of the prediction approach baed on kmean. One of the important parameter for thi algorithm i the number of cluter, k. If k i too mall uer profile with little imilarity are clutered together, reducing the accuracy of the algorithm; on the other hand, if k i too high the calability of the algorithm (one of it main expected advantage over the knn baed algorithm) can be negatively affected. In the experiment preented in thi ection k wa et to 8, a value choen after ome 84
6 preliminary tet demontrated that it i a good choice for the data et ued. The neighborhood ued to predict a feedback to a target uer i formed by the cluter centroid that have a degree of imilarity to that uer (computed uing the PCC and PCCSS) greater than or equal to 0.8. A can be oberved in figure 3, the kmean prediction algorithm without ervice imilarity ha a prediction error ignificantly lower than that which i obtained when applying the IMEAN and UMEAN algorithm, except when the number of available core i very low (when 50 out of 56 are removed). Under uch circumtance, the mall number of available uer profile prevent the contruction of repreentative uer group, everely affecting the prediction accuracy of the algorithm. Under uch pare data condition, the ue of ervice imilarity account for an appreciable increae in accuracy. A already verified for the knn algorithm, the bet reult are oberved when ervice imilarity information i ued for computing both the PCC and the WAAR. Thee reult can be explained in the ame manner a done for the knn algorithm: when running the algorithm without ervice imilarity information, everal imilar uer are not identified a uch and are not clutered together, becaue only uer that rated the ame et of ervice can be conidered imilar; when ervice imilarity i taken into account, it i alo poible to identify imilar uer among thoe uer that rated imilar ervice. In addition, when computing the WAAR, the ue of ervice imilarity information contribute to increae the accuracy becaue it allow for the conideration of more core to calculate a prediction. Figure 3 how that when 50 out of 56 core are removed, characterizing a ituation of carcity of evaluation, uing ervice imilarity for computing the PCC and WAAR account for a NMAE of 0.32, while when thi information i not ued the NMAE rie to 0.89, an increae of 178%. Comparing the KNN and the Kmean Prediction Algorithm The literature ay that memorybaed prediction algorithm, like thoe baed on the knn, often have greater accuracy than modelbaed algorithm, uch a thoe baed on the kmean, but modelbaed algorithm are more calable becaue they require le memory and are fater. Figure 4 confirm the firt claue of the NMAE UMEAN knn with ervice imilarity kmean w ith ervice imilarity 0.00 Number of rating removed knn w ithout ervice imilarity kmean without ervice imilarity Figure 4. Comparing the prediction performance of knn and kmean algorithm previou entence. However, it i worth noting that the k mean algorithm with ervice imilarity i more accurate than the knn one without ervice imilarity. The lower accuracy of the kmean algorithm with repect to knn can be explained by the fact that the k mean method ue cluter centroid and not the profile of imilar uer to predict the core. Profile are grouped into cluter baed on the imilarity of each profile to a cluter centroid; thu a poorly choen centroid directly influence the quality of the cluter. In the implementation decribed, the initial eight centroid were choen randomly among the available profile. The particularly bad reult for the kmean algorithm when many core are removed and imilar ervice are not conidered can be explained by the difficulty in finding imilar uer to group together when data i pare. Figure 5 how the time required by the algorithm to predict the removed core when uing a notebook with an Intel Core Duo 1.66 GHz proceor and 2 GB of RAM. Regarding the kmean algorithm, the required time for core prediction with already created cluter i hown. Under thee condition, the run time i lower for the kmean algorithm, particularly when the ueritem matrix i dene. Thi reult wa expected becaue a high number of profile are conidered in the computation of the PCC and the WAAR when uing the knn method, while only a mall number of cluter centroid are ued when applying the kmean method. NMAE IMEAN UMEAN Without ervice imilarity PCC w ith ervice imilarity WAAR w ith ervice imilarity PCC and WAAR w ith ervice imilarity Number of rating removed Time (m) KNN without ervice imilarity Kmean without ervice imilarity KNN with ervice imilarity Kmean with ervice imilarity Number of rating removed Figure 3. Impact of ervice imilarity on the prediction performance of the kmean algorithm Figure 5. Runtime of the knn and kmean algorithm for core prediction 85
DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENTMATCHING INTRUSION DETECTION SYSTEMS. G. Chapman J. Cleese E. Idle
DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENTMATCHING INTRUSION DETECTION SYSTEMS G. Chapman J. Cleee E. Idle ABSTRACT Content matching i a neceary component of any ignaturebaed network Intruion Detection
More informationDISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENTMATCHING INTRUSION DETECTION SYSTEMS
DISTRIBUTED DATA PARALLEL TECHNIQUES FOR CONTENTMATCHING INTRUSION DETECTION SYSTEMS Chritopher V. Kopek Department of Computer Science Wake Foret Univerity WintonSalem, NC, 2709 Email: kopekcv@gmail.com
More informationUnit 11 Using Linear Regression to Describe Relationships
Unit 11 Uing Linear Regreion to Decribe Relationhip Objective: To obtain and interpret the lope and intercept of the leat quare line for predicting a quantitative repone variable from a quantitative explanatory
More informationOptical Illusion. Sara Bolouki, Roger Grosse, Honglak Lee, Andrew Ng
Optical Illuion Sara Bolouki, Roger Groe, Honglak Lee, Andrew Ng. Introduction The goal of thi proect i to explain ome of the illuory phenomena uing pare coding and whitening model. Intead of the pare
More informationA Spam Message Filtering Method: focus on run time
, pp.2933 http://dx.doi.org/10.14257/atl.2014.76.08 A Spam Meage Filtering Method: focu on run time SinEon Kim 1, JungTae Jo 2, SangHyun Choi 3 1 Department of Information Security Management 2 Department
More informationA technical guide to 2014 key stage 2 to key stage 4 value added measures
A technical guide to 2014 key tage 2 to key tage 4 value added meaure CONTENTS Introduction: PAGE NO. What i value added? 2 Change to value added methodology in 2014 4 Interpretation: Interpreting chool
More informationREDUCTION OF TOTAL SUPPLY CHAIN CYCLE TIME IN INTERNAL BUSINESS PROCESS OF REAMER USING DOE AND TAGUCHI METHODOLOGY. Abstract. 1.
International Journal of Advanced Technology & Engineering Reearch (IJATER) REDUCTION OF TOTAL SUPPLY CHAIN CYCLE TIME IN INTERNAL BUSINESS PROCESS OF REAMER USING DOE AND Abtract TAGUCHI METHODOLOGY Mr.
More informationFEDERATION OF ARAB SCIENTIFIC RESEARCH COUNCILS
Aignment Report RP/98983/5/0./03 Etablihment of cientific and technological information ervice for economic and ocial development FOR INTERNAL UE NOT FOR GENERAL DITRIBUTION FEDERATION OF ARAB CIENTIFIC
More informationProject Management Basics
Project Management Baic A Guide to undertanding the baic component of effective project management and the key to ucce 1 Content 1.0 Who hould read thi Guide... 3 1.1 Overview... 3 1.2 Project Management
More informationHeuristic Approach to Dynamic Data Allocation in Distributed Database Systems
Pakitan Journal of Information and Technology 2 (3): 231239, 2003 ISSN 16826027 2003 Aian Network for Scientific Information Heuritic Approach to Dynamic Data Allocation in Ditributed Databae Sytem 1
More information1 Introduction. Reza Shokri* Privacy Games: Optimal UserCentric Data Obfuscation
Proceeding on Privacy Enhancing Technologie 2015; 2015 (2):1 17 Reza Shokri* Privacy Game: Optimal UerCentric Data Obfucation Abtract: Conider uer who hare their data (e.g., location) with an untruted
More informationSCM integration: organiational, managerial and technological iue M. Caridi 1 and A. Sianei 2 Dipartimento di Economia e Produzione, Politecnico di Milano, Italy Email: maria.caridi@polimi.it Itituto
More informationClusterAware Cache for Network Attached Storage *
CluterAware Cache for Network Attached Storage * Bin Cai, Changheng Xie, and Qiang Cao National Storage Sytem Laboratory, Department of Computer Science, Huazhong Univerity of Science and Technology,
More informationPerformance of a BrowserBased JavaScript Bandwidth Test
Performance of a BrowerBaed JavaScript Bandwidth Tet David A. Cohen II May 7, 2013 CP SC 491/H495 Abtract An exiting browerbaed bandwidth tet written in JavaScript wa modified for the purpoe of further
More informationQueueing systems with scheduled arrivals, i.e., appointment systems, are typical for frontal service systems,
MANAGEMENT SCIENCE Vol. 54, No. 3, March 28, pp. 565 572 in 25199 ein 1526551 8 543 565 inform doi 1.1287/mnc.17.82 28 INFORMS Scheduling Arrival to Queue: A SingleServer Model with NoShow INFORMS
More informationCHARACTERISTICS OF WAITING LINE MODELS THE INDICATORS OF THE CUSTOMER FLOW MANAGEMENT SYSTEMS EFFICIENCY
Annale Univeritati Apuleni Serie Oeconomica, 2(2), 200 CHARACTERISTICS OF WAITING LINE MODELS THE INDICATORS OF THE CUSTOMER FLOW MANAGEMENT SYSTEMS EFFICIENCY Sidonia Otilia Cernea Mihaela Jaradat 2 Mohammad
More informationA note on profit maximization and monotonicity for inbound call centers
A note on profit maximization and monotonicity for inbound call center Ger Koole & Aue Pot Department of Mathematic, Vrije Univeriteit Amterdam, The Netherland 23rd December 2005 Abtract We conider an
More informationGroup Mutual Exclusion Based on Priorities
Group Mutual Excluion Baed on Prioritie Karina M. Cenci Laboratorio de Invetigación en Sitema Ditribuido Univeridad Nacional del Sur Bahía Blanca, Argentina kmc@c.un.edu.ar and Jorge R. Ardenghi Laboratorio
More informationCASE STUDY BRIDGE. www.futureprocessing.com
CASE STUDY BRIDGE TABLE OF CONTENTS #1 ABOUT THE CLIENT 3 #2 ABOUT THE PROJECT 4 #3 OUR ROLE 5 #4 RESULT OF OUR COLLABORATION 67 #5 THE BUSINESS PROBLEM THAT WE SOLVED 8 #6 CHALLENGES 9 #7 VISUAL IDENTIFICATION
More informationAN OVERVIEW ON CLUSTERING METHODS
IOSR Journal Engineering AN OVERVIEW ON CLUSTERING METHODS T. Soni Madhulatha Aociate Preor, Alluri Intitute Management Science, Warangal. ABSTRACT Clutering i a common technique for tatitical data analyi,
More informationBiObjective Optimization for the Clinical Trial Supply Chain Management
Ian David Lockhart Bogle and Michael Fairweather (Editor), Proceeding of the 22nd European Sympoium on Computer Aided Proce Engineering, 1720 June 2012, London. 2012 Elevier B.V. All right reerved. BiObjective
More informationAssessing the Discriminatory Power of Credit Scores
Aeing the Dicriminatory Power of Credit Score Holger Kraft 1, Gerald Kroiandt 1, Marlene Müller 1,2 1 Fraunhofer Intitut für Techno und Wirtchaftmathematik (ITWM) GottliebDaimlerStr. 49, 67663 Kaierlautern,
More informationProfitability of Loyalty Programs in the Presence of Uncertainty in Customers Valuations
Proceeding of the 0 Indutrial Engineering Reearch Conference T. Doolen and E. Van Aken, ed. Profitability of Loyalty Program in the Preence of Uncertainty in Cutomer Valuation Amir Gandomi and Saeed Zolfaghari
More informationUtilityBased Flow Control for Sequential Imagery over Wireless Networks
UtilityBaed Flow Control for Sequential Imagery over Wirele Networ Tomer Kihoni, Sara Callaway, and Mar Byer Abtract Wirele enor networ provide a unique et of characteritic that mae them uitable for building
More informationStatespace analysis of control systems: Part I
Why a different approach? Statepace analyi of control ytem: Part I Uing a tatevariable approach give u a traightforward way to analyze MIM multipleinput, multiple output ytem. A tate variable model
More informationExposure Metering Relating Subject Lighting to Film Exposure
Expoure Metering Relating Subject Lighting to Film Expoure By Jeff Conrad A photographic expoure meter meaure ubject lighting and indicate camera etting that nominally reult in the bet expoure of the film.
More informationINFORMATION Technology (IT) infrastructure management
IEEE TRANSACTIONS ON CLOUD COMPUTING, VOL. 2, NO. 1, MAY 214 1 BuineDriven Longterm Capacity Planning for SaaS Application David Candeia, Ricardo Araújo Santo and Raquel Lope Abtract Capacity Planning
More informationPartial optimal labeling search for a NPhard subclass of (max,+) problems
Partial optimal labeling earch for a NPhard ubcla of (max,+) problem Ivan Kovtun International Reearch and Training Center of Information Technologie and Sytem, Kiev, Uraine, ovtun@image.iev.ua Dreden
More informationLicense & SW Asset Management at CES Design Services
Licene & SW Aet Management at CES Deign Service johann.poechl@iemen.com www.cesdeignservice.com 2003 Siemen AG Öterreich Overview 1. Introduction CES Deign Service 2. Objective and Motivation 3. What
More informationSoftware Engineering Management: strategic choices in a new decade
Software Engineering : trategic choice in a new decade Barbara Farbey & Anthony Finkeltein Univerity College London, Department of Computer Science, Gower St. London WC1E 6BT, UK {b.farbey a.finkeltein}@ucl.ac.uk
More informationINTERACTIVE TOOL FOR ANALYSIS OF TIMEDELAY SYSTEMS WITH DEADTIME COMPENSATORS
INTERACTIVE TOOL FOR ANALYSIS OF TIMEDELAY SYSTEMS WITH DEADTIME COMPENSATORS Joé Lui Guzmán, Pedro García, Tore Hägglund, Sebatián Dormido, Pedro Alberto, Manuel Berenguel Dep. de Lenguaje y Computación,
More informationGrowing SelfOrganizing Maps for Surface Reconstruction from Unstructured Point Clouds
Growing SelfOrganizing Map for Surface Recontruction from Untructured Point Cloud Renata L. M. E. do Rêgo, Aluizio F. R. Araújo, and Fernando B.de Lima Neto Abtract Thi work introduce a new method for
More informationRisk Management for a Global Supply Chain Planning under Uncertainty: Models and Algorithms
Rik Management for a Global Supply Chain Planning under Uncertainty: Model and Algorithm Fengqi You 1, John M. Waick 2, Ignacio E. Gromann 1* 1 Dept. of Chemical Engineering, Carnegie Mellon Univerity,
More informationNETWORK TRAFFIC ENGINEERING WITH VARIED LEVELS OF PROTECTION IN THE NEXT GENERATION INTERNET
Chapter 1 NETWORK TRAFFIC ENGINEERING WITH VARIED LEVELS OF PROTECTION IN THE NEXT GENERATION INTERNET S. Srivatava Univerity of Miouri Kana City, USA hekhar@conrel.ice.umkc.edu S. R. Thirumalaetty now
More informationMODIFIED 2D FINITEDIFFERENCE TIMEDOMAIN TECHNIQUE FOR TUNNEL PATH LOSS PREDICTION. Y. Wu, M. Lin and I.J. Wassell
2 nd International Conference on Wirele Communication in Underground and Confined Area Augut 2527, 2008 Vald Or  Québec  Canada MODIFIED 2D FINITEDIFFERENCE TIMEDOMAIN TECHNIQUE FOR TUNNEL PATH LOSS
More informationControl of Wireless Networks with Flow Level Dynamics under Constant Time Scheduling
Control of Wirele Network with Flow Level Dynamic under Contant Time Scheduling Long Le and Ravi R. Mazumdar Department of Electrical and Computer Engineering Univerity of Waterloo,Waterloo, ON, Canada
More informationSupport Vector Machine Based Electricity Price Forecasting For Electricity Markets utilising Projected Assessment of System Adequacy Data.
The Sixth International Power Engineering Conference (IPEC23, 2729 November 23, Singapore Support Vector Machine Baed Electricity Price Forecating For Electricity Maret utiliing Projected Aement of Sytem
More informationROBURST: A Robust Virtualization Cost Model for Workload Consolidation over Clouds
!111! 111!ttthhh IIIEEEEEEEEE///AAACCCMMM IIInnnttteeerrrnnnaaatttiiiooonnnaaalll SSSyyymmmpppoooiiiuuummm ooonnn CCCllluuuttteeerrr,,, CCClllooouuuddd aaannnddd GGGrrriiiddd CCCooommmpppuuutttiiinnnggg
More informationA Note on Profit Maximization and Monotonicity for Inbound Call Centers
OPERATIONS RESEARCH Vol. 59, No. 5, September October 2011, pp. 1304 1308 in 0030364X ein 15265463 11 5905 1304 http://dx.doi.org/10.1287/opre.1110.0990 2011 INFORMS TECHNICAL NOTE INFORMS hold copyright
More informationBidding for Representative Allocations for Display Advertising
Bidding for Repreentative Allocation for Diplay Advertiing Arpita Ghoh, Preton McAfee, Kihore Papineni, and Sergei Vailvitkii Yahoo! Reearch. {arpita, mcafee, kpapi, ergei}@yahooinc.com Abtract. Diplay
More informationChapter 4: MeanVariance Analysis
Chapter 4: MeanVariance Analyi Modern portfolio theory identifie two apect of the invetment problem. Firt, an invetor will want to maximize the expected rate of return on the portfolio. Second, an invetor
More informationApigee Edge: Apigee Cloud vs. Private Cloud. Evaluating deployment models for API management
Apigee Edge: Apigee Cloud v. Private Cloud Evaluating deployment model for API management Table of Content Introduction 1 Time to ucce 2 Total cot of ownerhip 2 Performance 3 Security 4 Data privacy 4
More informationMixed Method of Model Reduction for Uncertain Systems
SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol 4 No June Mixed Method of Model Reduction for Uncertain Sytem N Selvaganean Abtract: A mixed method for reducing a higher order uncertain ytem to a table reduced
More informationPekka Helkiö, 58490K Antti Seppälä, 63212W Ossi Syd, 63513T
Pekka Helkiö, 58490K Antti Seppälä, 63212W Oi Syd, 63513T Table of Content 1. Abtract...1 2. Introduction...2 2.1 Background... 2 2.2 Objective and Reearch Problem... 2 2.3 Methodology... 2 2.4 Scoping
More informationMANAGING DATA REPLICATION IN MOBILE AD HOC NETWORK DATABASES (Invited Paper) *
MANAGING DATA REPLICATION IN MOBILE AD HOC NETWORK DATABASES (Invited Paper) * Praanna Padmanabhan School of Computer Science The Univerity of Oklahoma Norman OK, USA praannap@yahooinc.com Dr. Le Gruenwald
More informationPOSSIBILITIES OF INDIVIDUAL CLAIM RESERVE RISK MODELING
POSSIBILITIES OF INDIVIDUAL CLAIM RESERVE RISK MODELING Pavel Zimmermann * 1. Introduction A ignificant increae in demand for inurance and financial rik quantification ha occurred recently due to the fact
More informationQueueing Models for Multiclass Call Centers with RealTime Anticipated Delays
Queueing Model for Multicla Call Center with RealTime Anticipated Delay Oualid Jouini Yve Dallery Zeynep Akşin Ecole Centrale Pari Koç Univerity Laboratoire Génie Indutriel College of Adminitrative Science
More informationImagery Portal Workshop #2 Department of Administrative Services, Executive Building Salem, Oregon May 11, 2006
ry Portal Workhop #2 Department of Adminitrative Service, Executive Building Salem, Oregon May 11, 2006 Workhop Purpoe: dicu the outcome of the phae 1 coping proce for development of an imagery portal
More informationPeriodic Symmetric Functions and Addition Related Arithmetic Operations in Single Electron Tunneling Technology
Periodic Symmetric Function and Addition Related Arithmetic Operation in Single Electron Tunneling Technology or Meenderinck Sorin otofana omputer Engineering Lab, Delft Univerity of Technology, Delft,
More informationRISK MANAGEMENT POLICY
RISK MANAGEMENT POLICY The practice of foreign exchange (FX) rik management i an area thrut into the potlight due to the market volatility that ha prevailed for ome time. A a conequence, many corporation
More informationCLUSTBIGFIMFREQUENT ITEMSET MINING OF BIG DATA USING PREPROCESSING BASED ON MAPREDUCE FRAMEWORK
CLUSTBIGFIMFREQUENT ITEMSET MINING OF BIG DATA USING PREPROCESSING BASED ON MAPREDUCE FRAMEWORK Sheela Gole 1 and Bharat Tidke 2 1 Department of Computer Engineering, Flora Intitute of Technology, Pune,
More informationTrusted Document Signing based on use of biometric (Face) keys
Truted Document Signing baed on ue of biometric (Face) Ahmed B. Elmadani Department of Computer Science Faculty of Science Sebha Univerity Sebha Libya www.ebhau.edu.ly elmadan@yahoo.com ABSTRACT An online
More informationTRADING rules are widely used in financial market as
Complex Stock Trading Strategy Baed on Particle Swarm Optimization Fei Wang, Philip L.H. Yu and David W. Cheung Abtract Trading rule have been utilized in the tock market to make profit for more than a
More informationChapter and. FIGURE 9 36 The deviation of an actual gasturbine cycle from the ideal Brayton cycle as a result of irreversibilities.
Chapter 9 The thermal efficiency could alo be determined from where h th q out q out h h 789.7 00.9 89. kj>kg Dicuion Under the coldairtard aumption (contant pecific heat value at room temperature),
More informationThus far. Inferences When Comparing Two Means. Testing differences between two means or proportions
Inference When Comparing Two Mean Dr. Tom Ilvento FREC 48 Thu far We have made an inference from a ingle ample mean and proportion to a population, uing The ample mean (or proportion) The ample tandard
More informationAlgorithms for Advance Bandwidth Reservation in Media Production Networks
Algorithm for Advance Bandwidth Reervation in Media Production Network Maryam Barhan 1, Hendrik Moen 1, Jeroen Famaey 2, Filip De Turck 1 1 Department of Information Technology, Ghent Univerity imind Gaton
More informationChapter 10 Stocks and Their Valuation ANSWERS TO ENDOFCHAPTER QUESTIONS
Chapter Stoc and Their Valuation ANSWERS TO ENOFCHAPTER QUESTIONS  a. A proxy i a document giving one peron the authority to act for another, typically the power to vote hare of common toc. If earning
More informationTowards ControlRelevant Forecasting in Supply Chain Management
25 American Control Conference June 81, 25. Portland, OR, USA WeA7.1 Toward ControlRelevant Forecating in Supply Chain Management Jay D. Schwartz, Daniel E. Rivera 1, and Karl G. Kempf Control Sytem
More informationLaureate Network Products & Services Copyright 2013 Laureate Education, Inc.
Laureate Network Product & Service Copyright 2013 Laureate Education, Inc. KEY Coure Name Laureate Faculty Development...3 Laureate Englih Program...9 Language Laureate Signature Product...12 Length Laureate
More informationRedesigning Ratings: Assessing the Discriminatory Power of Credit Scores under Censoring
Redeigning Rating: Aeing the Dicriminatory Power of Credit Score under Cenoring Holger Kraft, Gerald Kroiandt, Marlene Müller Fraunhofer Intitut für Techno und Wirtchaftmathematik (ITWM) Thi verion: June
More informationPerformance of Multiple TFRC in Heterogeneous Wireless Networks
Performance of Multiple TFRC in Heterogeneou Wirele Network 1 HyeonJin Jeong, 2 SeongSik Choi 1, Firt Author Computer Engineering Department, Incheon National Univerity, oaihjj@incheon.ac.kr *2,Correponding
More informationSELFMANAGING PERFORMANCE IN APPLICATION SERVERS MODELLING AND DATA ARCHITECTURE
SELFMANAGING PERFORMANCE IN APPLICATION SERVERS MODELLING AND DATA ARCHITECTURE RAVI KUMAR G 1, C.MUTHUSAMY 2 & A.VINAYA BABU 3 1 HP Bangalore, Reearch Scholar JNTUH, Hyderabad, India, 2 Yahoo, Bangalore,
More informationTIME SERIES ANALYSIS AND TRENDS BY USING SPSS PROGRAMME
TIME SERIES ANALYSIS AND TRENDS BY USING SPSS PROGRAMME RADMILA KOCURKOVÁ Sileian Univerity in Opava School of Buine Adminitration in Karviná Department of Mathematical Method in Economic Czech Republic
More informationBenchmarking BottomUp and TopDown Strategies for SPARQLtoSQL Query Translation
Benchmarking BottomUp and TopDown Strategie for SPARQLtoSQL Query Tranlation Kahlev a, Chebotko b,c, John Abraham b, Pearl Brazier b, and Shiyong Lu a a Department of Computer Science, Wayne State
More informationMaximizing Acceptance Probability for Active Friending in Online Social Networks
Maximizing for Active Friending in Online Social Network DeNian Yang, HuiJu Hung, WangChien Lee, Wei Chen Academia Sinica, Taipei, Taiwan The Pennylvania State Univerity, State College, Pennylvania,
More informationProcessor Cooling. Report for the practical course Chemieingenieurwesen I WS06/07. Zürich, January 16,
Proceor Cooling Report for the practical coure Chemieingenieurween I WS06/07 Zürich, January 16, 2007 Student: Francico Joé Guerra Millán fguerram@tudent.ethz.ch Andrea Michel michela@tudent.ethz.ch Aitant:
More informationTwo Dimensional FEM Simulation of Ultrasonic Wave Propagation in Isotropic Solid Media using COMSOL
Excerpt from the Proceeding of the COMSO Conference 0 India Two Dimenional FEM Simulation of Ultraonic Wave Propagation in Iotropic Solid Media uing COMSO Bikah Ghoe *, Krihnan Balaubramaniam *, C V Krihnamurthy
More informationSector Concentration in Loan Portfolios and Economic Capital. Abstract
Sector Concentration in Loan Portfolio and Economic Capital Klau Düllmann and Nancy Machelein 2 Thi verion: September 2006 Abtract The purpoe of thi paper i to meaure the potential impact of buineector
More informationCASE STUDY ALLOCATE SOFTWARE
CASE STUDY ALLOCATE SOFTWARE allocate caetud y TABLE OF CONTENTS #1 ABOUT THE CLIENT #2 OUR ROLE #3 EFFECTS OF OUR COOPERATION #4 BUSINESS PROBLEM THAT WE SOLVED #5 CHALLENGES #6 WORKING IN SCRUM #7 WHAT
More informationMobile Network Configuration for Largescale Multimedia Delivery on a Single WLAN
Mobile Network Configuration for Largecale Multimedia Delivery on a Single WLAN Huigwang Je, Dongwoo Kwon, Hyeonwoo Kim, and Hongtaek Ju Dept. of Computer Engineering Keimyung Univerity Daegu, Republic
More informationProceedings of Power Tech 2007, July 15, Lausanne
Second Order Stochatic Dominance Portfolio Optimization for an Electric Energy Company M.P. Cheong, Student Member, IEEE, G. B. Sheble, Fellow, IEEE, D. Berleant, Senior Member, IEEE and C.C. Teoh, Student
More informationProgress 8 measure in 2016, 2017, and 2018. Guide for maintained secondary schools, academies and free schools
Progre 8 meaure in 2016, 2017, and 2018 Guide for maintained econdary chool, academie and free chool July 2016 Content Table of figure 4 Summary 5 A ummary of Attainment 8 and Progre 8 5 Expiry or review
More informationDespeckling Synthetic Aperture Radar Images with Cloud Computing using Graphics Processing Units
Depeckling Synthetic Aperture Radar Image with Cloud Computing uing Graphic Proceing Unit Matej Keneman 1, Dušan Gleich, Amor Chowdhury 1 1 Margento R & D d.o.o., Gopovetka ceta 84, Maribor, Slovenia matej.keneman@gmail.com
More informationSRA SOLOMON : MUC4 TEST RESULTS AND ANALYSI S
SRA SOLOMON : MUC4 TEST RESULTS AND ANALYSI S Chinatu Aone, Doug McKee, Sandy Shinn, Hatte Bleje r Sytem Reearch and Application (SRA ) 2000 15th Street North Arlington, VA 2220 1 aonec@ra.com INTRODUCTION
More informationCombining Statistics and Semantics via Ensemble Model for Document Clustering
ombining tatitic and emantic via Enemble Model or Document lutering amah Jamal Fodeh Michigan tate Univerity Eat Laning, MI, 48824 odeham@mu.edu William F Punch Michigan tate Univerity Eat Laning, MI,
More informationBUILTIN DUAL FREQUENCY ANTENNA WITH AN EMBEDDED CAMERA AND A VERTICAL GROUND PLANE
Progre In Electromagnetic Reearch Letter, Vol. 3, 51, 08 BUILTIN DUAL FREQUENCY ANTENNA WITH AN EMBEDDED CAMERA AND A VERTICAL GROUND PLANE S. H. ZainudDeen Faculty of Electronic Engineering Menoufia
More informationA Resolution Approach to a Hierarchical Multiobjective Routing Model for MPLS Networks
A Reolution Approach to a Hierarchical Multiobjective Routing Model for MPLS Networ Joé Craveirinha a,c, Rita GirãoSilva a,c, João Clímaco b,c, Lúcia Martin a,c a b c DEECFCTUC FEUC INESCCoimbra International
More informationOptimization Model of Higher Education Resources Allocation Based on Genetic Algorithm
Management cience and ngineering Vol. 7, No. 3, 203, pp. 7680 DOI:0.3968/j.me.93035X2030703.2622 IN 93034 [Print] IN 93035X [Online] www.ccanada.net www.ccanada.org Optimization Model of Higher ducation
More informationMultiObjective Optimization for Sponsored Search
MultiObjective Optimization for Sponored Search Yilei Wang 1,*, Bingzheng Wei 2, Jun Yan 2, Zheng Chen 2, Qiao Du 2,3 1 Yuanpei College Peking Univerity Beijing, China, 100871 (+86)15120078719 wangyileipku@gmail.com
More informationSocially Optimal Pricing of Cloud Computing Resources
Socially Optimal Pricing of Cloud Computing Reource Ihai Menache Microoft Reearch New England Cambridge, MA 02142 timena@microoft.com Auman Ozdaglar Laboratory for Information and Deciion Sytem Maachuett
More informationA Review On Software Testing In SDlC And Testing Tools
www.ijec.in International Journal Of Engineering And Computer Science ISSN:23197242 Volume  3 Iue 9 September, 2014 Page No. 81888197 A Review On Software Teting In SDlC And Teting Tool T.Amruthavalli*,
More informationControl Theory based Approach for the Improvement of Integrated Business Process Interoperability
www.ijcsi.org 201 Control Theory baed Approach for the Improvement of Integrated Buine Proce Interoperability Abderrahim Taoudi 1, Bouchaib Bounabat 2 and Badr Elmir 3 1 AlQualadi Reearch & Development
More informationOptimizing a Semantic Comparator using CUDAenabled Graphics Hardware
Optimizing a Semantic Comparator uing CUDAenabled Graphic Hardware Aalap Tripathy Suneil Mohan, Rabi Mahapatra Embedded Sytem and Codeign Lab codeign.ce.tamu.edu (Preented at ICSC 0, September 9, 0 in
More informationFour Ways Companies Can Use Open Source Social Publishing Tools to Enhance Their Business Operations
Four Way Companie Can Ue Open Source Social Publihing Tool to Enhance Their Buine Operation acquia.com 888.922.7842 1.781.238.8600 25 Corporate Drive, Burlington, MA 01803 Four Way Companie Can Ue Open
More informationGrowth and Sustainability of Managed Security Services Networks: An Economic Perspective
Growth and Sutainability of Managed Security Service etwork: An Economic Perpective Alok Gupta Dmitry Zhdanov Department of Information and Deciion Science Univerity of Minneota Minneapoli, M 55455 (agupta,
More informationHarmonic Oscillations / Complex Numbers
Harmonic Ocillation / Complex Number Overview and Motivation: Probably the ingle mot important problem in all of phyic i the imple harmonic ocillator. It can be tudied claically or uantum mechanically,
More informationLaboratory 3 Diode Characteristics
Laboratory 3 Diode Characteritic BACKGROUND A diode i a nonlinear, two terminal emiconductor device. he two terminal are the anode and the cathode. he circuit ymbol of a diode i depicted in Fig. 31.
More informationProgress 8 and Attainment 8 measure in 2016, 2017, and 2018. Guide for maintained secondary schools, academies and free schools
Progre 8 and Attainment 8 meaure in 2016, 2017, and 2018 Guide for maintained econdary chool, academie and free chool September 2016 Content Table of figure 4 Summary 5 A ummary of Attainment 8 and Progre
More informationOriginal Article: TOWARDS FLUID DYNAMICS EQUATIONS
Peer Reviewed, Open Acce, Free Online Journal Publihed monthly : ISSN: 88X Iue 4(5); April 15 Original Article: TOWARDS FLUID DYNAMICS EQUATIONS Citation Zaytev M.L., Akkerman V.B., Toward Fluid Dynamic
More informationpublished in Statistics and Probability Letters, 78, , 2008 Michael Lechner * SIAW
publihed in Statitic and Probability Letter, 78, 995, 28 A NOTE ON ENDOGENOUS CONTROL VARIABLES IN CAUSAL STUDIES Michael Lechner * SIAW Thi verion: March, 27 Date thi verion ha been printed: 8 May 27
More informationMorningstar Fixed Income Style Box TM Methodology
Morningtar Fixed Income Style Box TM Methodology Morningtar Methodology Paper Augut 3, 00 00 Morningtar, Inc. All right reerved. The information in thi document i the property of Morningtar, Inc. Reproduction
More informationUsing Graph Analysis to Study Networks of Adaptive Agent
Uing Graph Analyi to Study Network of Adaptive Agent Sherief Abdallah Britih Univerity in Dubai, United Arab Emirate Univerity of Edinburgh, United Kingdom hario@ieee.org ABSTRACT Experimental analyi of
More informationComputing Location from Ambient FM Radio Signals
Computing Location from Ambient FM Radio Signal Adel Youef Department of Computer Science Univerity of Maryland A.V. William Building College Park, MD 20742 adel@c.umd.edu John Krumm, Ed Miller, Gerry
More informationReturn on Investment and Effort Expenditure in the Software Development Environment
International Journal of Applied Information ytem (IJAI) IN : 22490868 Return on Invetment and Effort Expenditure in the oftware Development Environment Dineh Kumar aini Faculty of Computing and IT, ohar
More informationSimulation of Sensorless Speed Control of Induction Motor Using APFO Technique
International Journal of Computer and Electrical Engineering, Vol. 4, No. 4, Augut 2012 Simulation of Senorle Speed Control of Induction Motor Uing APFO Technique T. Raghu, J. Sriniva Rao, and S. Chandra
More informationv = x t = x 2 x 1 t 2 t 1 The average speed of the particle is absolute value of the average velocity and is given Distance travelled t
Chapter 2 Motion in One Dimenion 2.1 The Important Stuff 2.1.1 Poition, Time and Diplacement We begin our tudy of motion by conidering object which are very mall in comparion to the ize of their movement
More informationAccelerationDisplacement Crash Pulse Optimisation A New Methodology to Optimise Vehicle Response for Multiple Impact Speeds
AccelerationDiplacement Crah Pule Optimiation A New Methodology to Optimie Vehicle Repone for Multiple Impact Speed D. Gildfind 1 and D. Ree 2 1 RMIT Univerity, Department of Aeropace Engineering 2 Holden
More information1 Looking in the wrong place for healthcare improvements: A system dynamics study of an accident and emergency department
1 Looking in the wrong place for healthcare improvement: A ytem dynamic tudy of an accident and emergency department DC Lane, C Monefeldt and JV Roenhead  The London School of Economic and Political Science
More informationGrowth and Sustainability of Managed Security Services Networks: An Economic Perspective
Growth and Sutainability of Managed Security Service etwork: An Economic Perpective Alok Gupta Dmitry Zhdanov Department of Information and Deciion Science Univerity of Minneota Minneapoli, M 55455 (agupta,
More informationOPINION PIECE. It s up to the customer to ensure security of the Cloud
OPINION PIECE It up to the cutomer to enure ecurity of the Cloud Content Don t outource what you don t undertand 2 The check lit 2 Step toward control 4 Due Diligence 4 Contract 4 Edicovery 4 Standard
More information