[IEEE 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) - Seattle, WA, USA (2011.03.21-2011.03.25)] 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) - An approach to Collaborative Context Prediction

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<ul><li><p>An Approach to Collaborative Context Prediction</p><p>Christian Voigtmann, Sian Lun Lau, Klaus David</p><p>Department of Computer Science</p><p>University of Kassel, Germany</p><p>{voigtmann, slau, david}@uni-kassel.de</p><p>AbstractContext prediction approaches forecast future con-texts based on known context patterns to adapt e.g., services inadvance. In the case of the users context history not providingsuitable context information for the observed context pattern, tothe best of our knowledge context prediction algorithms will failto forecast the appropriate future context. To overcome the gapof missing context information in the users context history, wepropose the Collaborative Context Prediction (CCP) approach.CCP utilises the collaborative characteristics of existing recom-mendation systems of social networks. To evaluate the CCPmethod an experimental comparison of the proposed methodagainst the local Alignment context predictor is carried out.</p><p>Index Termscontext prediction, context awareness, tensordecomposition, hosvd, collaborative.</p><p>I. INTRODCTION</p><p>One interesting research issue in the field of context-aware</p><p>systems and environments is context prediction. Based on the</p><p>available context data, such systems and environments predict</p><p>future context of the user. With the help of this predicted</p><p>information, users in ubiquitous environments can be assisted</p><p>to a greater extend in different ways. Taking for example, a</p><p>research assistant who presents the progress of his work in</p><p>the same room every week. Before he enters the room for</p><p>his next presentation, the context-aware system automatically</p><p>adapts the designated services in the room to be ready for</p><p>his presentation using a prediction system spanning the whole</p><p>university. Possible context information useful for the predic-</p><p>tion are his movement patterns or devices he has used in the</p><p>past. A common approach to enable the prediction of future</p><p>context is to make use of the gathered and stored contexts</p><p>related to the users actions or to the users environment. The</p><p>information is needed by a context prediction algorithm to</p><p>forecast contexts for a given context pattern. However, if the</p><p>research assistant gives his presentation in a room he has not</p><p>been in before, his current movement patterns are unknown</p><p>to the context prediction system. Current context prediction</p><p>approaches that rely on the context history of a single user</p><p>fail to forecast the users next context - doing a presentation.</p><p>Therefore, the adaptations would not take place.</p><p>To overcome the problem of missing context information in</p><p>a single users context history, we utilise a collaboration-based</p><p>technique. We propose including and utilising the context</p><p>histories from other users to solve the problem of missing</p><p>context patterns in a users context history. We call this idea</p><p>Collaboration-based Context Prediction (CCP). To enrich the</p><p>context history of the user, we use the Higher Order Singular</p><p>Value Decomposition (HOSVD) method that has been applied</p><p>successfully in the field of tag recommendation [1]. With</p><p>regard to the above-mentioned scenario, the future context of</p><p>the research student - giving a presentation - which is based on</p><p>unknown movement patterns could be predicted using CCP, if</p><p>the context is provided by context histories of his colleagues</p><p>and if these histories show sufficient similarities to the research</p><p>students history.</p><p>Existing state of the art to context prediction is outlined in</p><p>the next Section. An introduction to the Collaboration-based</p><p>Context Predictor and to the underlying HOSVD technique is</p><p>outlined in Section III. Section IV gives a detailed example</p><p>to illustrate how the approach works. A proof of concept</p><p>regarding accuracy is given in Section V, by comparing the</p><p>CCP approach to an Alignment predictor which is based on the</p><p>Smith-Waterman-Algorithm. Section VI concludes the paper.</p><p>II. RELATED WORK</p><p>In this section we present existing state of the art context</p><p>prediction approaches briefly. One of the first projects that in-</p><p>cludes a mechanism to forecast future contexts is the Neuronal</p><p>Network House project directed by Michael C. Mozer [2]. In</p><p>this project, lifestyle information of occupants was gathered</p><p>to predict their next actions. Another smart home project</p><p>conducted by Diana J. Cook is called MavHome [3]. In the</p><p>case of this project the Active LeZi approach [4], a sequence-</p><p>matching algorithm, and the Episode Discovery algorithm [5]</p><p>have been developed to forecast, among others, devices the</p><p>inhabitants will be interacting next with. The authors in [6]</p><p>suggest a method to predict a users next location by using</p><p>and interpreting his or her GPS data. The introduced approach</p><p>clusters the gained GPS data to meaningful locations and uses</p><p>a Markov model for the prediction process.</p><p>A context time series prediction algorithm that is based on</p><p>local alignment techniques is introduced in [7]. This approach</p><p>is inspired by algorithms with a focus on computational</p><p>biology. On the one hand, the author evaluated his proposed</p><p>context prediction method by applying Alignment to a wind</p><p>power dataset to predict the wind power required in the future.</p><p>On the other hand, a dataset consisting of the information on</p><p>the location of a users mobile phone has been used to predict</p><p>the users location trajectory.</p><p>In [8] a Structured Context Prediction algorithm is pre-</p><p>sented. This approach tries to overcome the trade-off for</p><p>context prediction methods needing to be generic with being</p><p>efficient by using additional knowledge about the application</p><p>domain given by the developer at the time of design. The used</p><p>8th IEEE Workshop on Context Modeling and Reasoning</p><p>978-1-61284-937-9/11/$26.00 2011 IEEE 438</p></li><li><p>evaluation approach focuses on the availability prediction of</p><p>two services. The mentioned approaches only use the context</p><p>history of the user whose future context should be predicted</p><p>and do not explicitly make use of existing relations of users</p><p>context histories. Thus, the fact to use context histories of other</p><p>users to gain extra information for the forecasting process has</p><p>not been considered so far. In the next section our proposed</p><p>approach is described in more detail.</p><p>III. COLLABORATIVE-BASED CONTEXT PREDICTION</p><p>We propose the Collaborative-based Context Prediction</p><p>(CCP) approach that increases the possibility to make a</p><p>currently unknown context pattern of a user available for fore-</p><p>casting the next future context. The Collaborative Ubiquitous</p><p>Environment presented in Figure 1 forms the foundation for</p><p>the CCP approach. The environment consists of three different</p><p>entities. The first entity is represented by the set of users U U of the Collaborative Ubiquitous Environment, the secondby the set of possible context patterns Cp CP and the thirdby the set of predictable future contexts Fc FC. Thereforethe history of a user Ui is described by Hi CP FC.</p><p>U1</p><p>U2</p><p>U3</p><p>Un</p><p>Cp |Fc2 1 Cp |Fc5 9</p><p>Cp |Fc7 5 Cp |Fc8 j Cp |Fc3 2</p><p>Cp |Fc1 n Cp |Fcm-1 i</p><p>Cp |Fcm 1</p><p>Context HistoryUser</p><p>Collaborative Ubiquitous Environment</p><p>Cp |Fc2 1</p><p>Cp |Fcm 1</p><p>Cp |Fcm-1 i</p><p>Cp |Fc5 9</p><p>Cp |Fc8 j</p><p>Cp |Fc8 j</p><p>Cp |Fcm-1 iCp |Fcm-1 i</p><p>Fig. 1. Presents n users of a Collaborative Ubiquitous Environment with ndifferent context histories. Equal context parts are marked in the same colour.Every context part in the context history Hi of the user Ui consists of twoelements. Cp CP indicates the context pattern and Fc FC indicates thefuture context that follows the previous context pattern.</p><p>HOSVD is used to enrich the context history of the user with</p><p>additional latent information by using existing relations (equal</p><p>context parts) between the context histories of the users in the</p><p>Collaborative Ubiquitous Environment (see Figure 1). Latent</p><p>information comprises new context parts in the context history</p><p>of the user that were formally unknown and can be used to</p><p>infer the next future context. The basic idea of HOSVD is to</p><p>restrict the dimensionality of each entity of a Collaborative</p><p>Ubiquitous Environment to a specific size where each entity</p><p>only contains relevant, less noisy information by using the</p><p>n-mode product [9]. Afterwards, the downsized information</p><p>space is used to recalculate the Collaborative Ubiquitous</p><p>Environment, based on the most relevant information using</p><p>the n-mode product again.</p><p>U1</p><p>U2</p><p>U3</p><p>Un</p><p>Cp1Cp2Cp3 Cpm</p><p>Fc1</p><p>Fc2</p><p>Fcj</p><p>11</p><p>1</p><p>1</p><p>1</p><p>1</p><p>Fig. 2. 3-order tensor to store data of a Collaborative Ubiquitous Envi-ronment. The first dimension characterises all users U of the CollaborativeUbiquitous Environment that provide information in the form of context data.The second dimension represents all context patterns CP that are availablein the context histories of the users. FC symbolises the third dimension, thefuture contexts, that can be inferred as the next possible context of a userU U for a given context pattern.</p><p>c1</p><p>c2</p><p>c3</p><p>Fig. 3. Downsized 3-order tensor structure that contains only the mostrelevant data of every dimension.</p><p>To demonstrate the practical use of HOSVD we apply this</p><p>technique to the presented Ubiquitous Collaborative Environ-</p><p>ment. For the storage of the data we use a 3-order tensor</p><p>A |U||CP||FC|, compare Figure 2. The existing relationsbetween the entities U , CP, FC are stored in the tensorstructure A with a one. All relations that do not exist are</p><p>treated as values of zero.</p><p>To minimize the number of unknown context pattern in</p><p>the users history we downsize the dimensionality sizes of</p><p>the 3-order tensor structure A. As a result, we receive a</p><p>3-order tensor c1c2c3 whose three dimensions arereduced to the information that spans the space that contains</p><p>the most relevant information. The size of the dimension Uis reduced to c1, the size of the dimension CP is reducedto c2 and the third dimension FC is collapsed to the sizeof c3. Figure 3 shows the tensor - marked in red - with its</p><p>reduced dimensionality size. symbolises the approximation</p><p>of the tensorA. Afterwards HOSVD is used to retransform the</p><p>tensor to the initial size of the dimension of the tensor A by</p><p>reusing the n-mode product as described above. The resulting</p><p>tensorA |U||CP||FC| finally concludes new informationin terms of new relations between the three dimensions that</p><p>can be used to forecast the users next contexts.</p><p>IV. ILLUSTRATIVE EXAMPLE</p><p>In order to show how our approach works, the Collaborative</p><p>Context Predictor is applied to an illustrative example. This</p><p>example presupposes three users of a Collaborative Ubiquitous</p><p>Environment. Figure 4 presents the three context histories of</p><p>the users. Equal context parts are marked with the same color.</p><p>In contrast to the used datasets in our experimental section,</p><p>439</p></li><li><p>in this example context patterns are not mapped to multiple</p><p>future contexts. The size of the context parts is determined</p><p>to an equal window size of four. Therefore every context part</p><p>consists of three contexts and one future context marked in</p><p>bold. All in all there are five different context parts composing</p><p>of ten different contexts.</p><p>U1</p><p>U2</p><p>U3 BBCY BBDZ BCCY BBDZ EFGZ BBDZ EFGZ EFGZ</p><p>FFXY FFXY BBDZ BBDZ</p><p>ABCX ABCX BCCY ABCX BCCY EFGZ EFGZ</p><p>User</p><p>1 2 3 4 5 6 7 8</p><p>EFGZ</p><p>BBDZ BBDZ FFXY FFXY</p><p>ContextParts</p><p>Fig. 4. Sample dataset consisting of context histories of three users.</p><p>As we can see, the context history of U1 does not provide</p><p>information for the context pattern {FFX, BBD}, the historyof U2 does not provide information for the context pattern</p><p>{ABC, BCC, EFG} and in the history of U3 the patterns{ABC, FFX} havent been stored yet. Hence it is not possible,for example, to provide a prediction for the next sensed</p><p>context pattern for U1, if this context pattern is either FFX</p><p>or BBD. The approach takes advantage of direct and indirect</p><p>relations between the histories of the users. Direct relations</p><p>are characterised by equal context parts between two users.</p><p>Indirect relations between two users e.g., U1 and U2 exist, if</p><p>the following two conditions are fulfilled:</p><p> U1 and U2 do not have the same context parts,</p><p> U3 features similarities of both U1 and U2.</p><p>CCP first transforms the underlying Collaborative Ubiq-</p><p>uitous Environment to a 3-order tensor structure with size</p><p>A 353. (cf., Figure 5). Then HOSVD for tensordimensionality reduction is applied by reducing the dimension</p><p>size of the user to one. The resulting core tensor structure</p><p>is 153. By reapplying HOSVD to the core tensorstructure containing the approximated data the tensor structure</p><p>presented in Figure 6 containing new information is received.</p><p>The resulted tensor contains new relations between users,</p><p>context patterns and future contexts which are symbolised by</p><p>the new values. Because context patterns are not mapped to</p><p>multiple future contexts in this example the relations between</p><p>users, context patterns and future context in the resulted tensor</p><p>A are obvious. Otherwise, the future context with the highest</p><p>value that forms a relation to a given user and context pattern</p><p>will be predicted. For predicting future contexts for context</p><p>pattern FFX or BBD to U1, we can now use the new relations</p><p>provided by the resulted tensor to make a prediction. For</p><p>the pattern FFX we will predict Y and for BBD we will</p><p>predict the future context Z. The resulting prediction for BBD</p><p>is reasonable, because U1 shares the same context patterns</p><p>{EFG, BCC} with U3. The same applies to the predictionfor pattern FFX, because U1 has a direct relation to U3 and</p><p>U3 has a direct relation to U2 so U1 and U2 hold indirect</p><p>similarities. This illustrative example shows that CCP can be</p><p>used to provide further information to context histories of users</p><p>3</p><p>2</p><p>2</p><p>4 3</p><p>4</p><p>3</p><p>U1</p><p>U2</p><p>U3</p><p>XY</p><p>Z</p><p>AB</p><p>C</p><p>BC</p><p>C</p><p>FF</p><p>X</p><p>BB</p><p>D</p><p>EF</p><p>G</p><p>AB</p><p>C</p><p>BC</p><p>C</p><p>FF</p><p>X</p><p>BB</p><p>D</p><p>EF</p><p>G</p><p>AB</p><p>C</p><p>BC</p><p>C</p><p>FF</p><p>X</p><p>BB</p><p>D</p><p>EF</p><p>G</p><p>3</p><p>Fig. 5. Shows the tensor A 353 that contains the information ofthe used sample data. The tensor consists of three users, five different contextpatterns and three different future contexts.</p><p>1.3</p><p>1.8</p><p>U1</p><p>U2</p><p>U3</p><p>XY</p><p>Z</p><p>AB</p><p>C</p><p>BC</p><p>C</p><p>FF</p><p>X</p><p>BB</p><p>D</p><p>EF</p><p>G</p><p>AB</p><p>C</p><p>BC</p><p>C</p><p>FF</p><p>X</p><p>BB</p><p>D</p><p>EF</p><p>G</p><p>AB</p><p>C</p><p>BC</p><p>C</p><p>FF</p><p>X</p><p>BB</p><p>D</p><p>EF</p><p>G</p><p>0.4</p><p>1.4</p><p>0.6</p><p>1.9</p><p>0.5</p><p>0.2</p><p>0.6</p><p>2.1</p><p>0.7</p><p>2.2</p><p>2.7</p><p>0.9</p><p>2.9</p><p>Fig. 6. Presents the resulted tensorA 353 that contains new relationsbetween users, context patterns and future contexts.</p><p>by using existing direct or indirect relations to context histories</p><p>of other users. In the next section we will verify this result</p><p>by applying the CCP approach to a real dataset consisting of</p><p>movement patterns.</p><p>V. EXPERIMENTAL RESULTS</p><p>In this section we present experimental results of our CCP</p><p>approach that is based on real-world context data. We show</p><p>that context histories of additional users can be utilised to</p><p>allow context prediction even if the user himself, in this</p><p>case the research assistant in our introduction, does not have</p><p>suitable information in his history. Furthermore, we explore</p><p>how the prediction accuracy for so far unobserved context</p><p>patterns is affected by the number of used histories. Finally,</p><p>we compare the accuracy of our results to a local Alignment</p><p>context prediction approach that has access to the same context</p><p>history database using the Smith-Waterman-Algorithm [10].</p><p>A. Dataset</p><p>The dataset contains accelerat...</p></li></ul>