WS-Sky: An Efficient and Flexible Framework for QoS-Aware Web Service Selection

Transcription

1 202 IEEE Ninth International Conference on Services Computing WS-Sky: An Efficient and Flexible Framework for QoS-Aware Web Service Selection Karim Benouaret, Djamal Benslimane University of Lyon, LIRIS/CNRS Villeurbanne, France {karim.benouaret, Allel Hadjali Rennes University, ENSSAT/IRISA Lannion, France Abstract With the development of Service oriented Computing (SOC), more and more functionally similar Web services are deployed over the Web. Quality of service (QoS) aspects (e.g., availability, response time, etc.) are thus crucial for selecting among functionally similar Web services. Moreover, the skyline has been considered as an important concept for selecting Web service based on QoS. In this paper, we propose WS-Sky, a Web service selection framework based on QoS. Our framework leverages two variants of the notion of skyline to effectively and efficiently select Web services that better fit the user needs. Our experimental evaluation on both real-world and synthetic datasets demonstrates that WS-Sky assists users to find the most relevant Web services in a flexible way, and allows users to control the size of the retrieved Web services. Keywords-Web service selection; skyline analysis; QoS; I. INTRODUCTION Web services are software systems that have a well defined interface and perform a specific task. Typical examples include stateful Web services, altering the world state (e.g., an on-line shopping Web service or an on-line booking Web service), and stateless Web services, returning information to the user, (e.g., a weather Web service or news Web service). Web services can be published, located and invoked across the Web using a set of standards such as SOAP, WSDL and UDDI [], [2]. Formally, a Web service is described by the names and types of input and output parameters, constraints, preconditions and effects, as well as Quality of Service (QoS) attributes, such as price, response time, etc. Recently, there has been a large flow of Web services deployed over the Web, due to the acceptance of Service Oriented Architecture (SOA) as the solution to interoperation, reuse and globalization [3], [4]. The statistics published by the Web services search engine seekda! indicates an exponential increase in the number of accessible Web services over the last 67 months. In addition, according to [5], there has been more than 30% growth in the number of published Web services between October 2006 and October As the Web is populated with a large number of Web services, there may be multiple service providers competing to offer the same functionality, but with different QoS. QoS is thus a crucial criterion to select among functionally similar Web services. Table I: A set of functionally similar Web services Web service q ($) q 2 (ms) q 3 (ms) q 4 (%) is skyline s yes s yes s yes s yes s yes s yes s no s no s no s no Example. Consider a Search Engine Web service. Typically, there are too many Web services providing this functionality (e.g., Google Search, Amazon Search, etc.), but with different QoS. Assume that the Web services in Table I provide such functionality. For each Web service Table I sets its values on four QoS parameters q, q 2, q 3 and q 4, say price, response time, latency and accuracy (i.e., the rate of errors produced by the service), respectively. To select a satisfactory Web service, users usually need to examine all of them manually. This would be very painstaking. Computing the skyline comes a popular solution for eliminating candidate Web services [6], [7], [8] as it overcomes the major limitation of the current approaches that require users to assign weights over different QoS attributes. The skyline is a subset of Web services that are not (Pareto) dominated by any other Web service. A Web service s i is said to dominate another Web service s j if and only if s i is better than or equal to s j in all QoS parameters and better than s j in at least one QoS parameter. For instance, Web service s 4 dominates Web services s 7 and s 8.AWeb service that belongs to the skyline is said to be a skyline Web service. The column is skyline of Table I indicates if a Web service is (or not) a skyline Web service. As it can be seen the skyline comprises 60% of the candidate Web services. Also, the size of the skyline increases significantly with the increase of the number of QoS attributes since as the number of QoS attributes increases, /2 $ IEEE DOI.9/SCC

2 for any Web service s i, it is more likely there is another Web service s j where s i and s j are better than each other in different QoS attributes. Thus, it may be hard for users to make a good and quick selection. In addition, as mentioned in [8], the skyline may lose some interesting Web services. For example, Web services s 7 and s 8 are not in the skyline as they are both dominated by Web service s 4. Furthermore, s 4 may be currently unavailable (e.g., error 404). Users thus have to choose between the other Web services retuned by the skyline, while s 7 and s 8 may be more appropriate. Requirements. To overcome the above insufficiencies, a system may be required to meet the following requirements. (R ) Skyline size requirement: further reduce the size of the skyline when needed (case of large skyline set). (R 2 ) Skyline quality requirement: include some interesting Web services that are not in the skyline and eliminate Web services that are in the skyline but with a bad compromise between QoS parameters, i.e., those that are good in some QoS parameters and very bad in the other ones (e.g., s 3 ). Contribution. Motivated by this, we propose a novel concept called σ-dominant skyline based on an extension of the (Pareto) dominance relationship. Specifically, the σ-dominant skyline satisfies requirements R. In [8], we proposed the α-dominant skyline based on another extension of the (Pareto) dominance relationship. Specifically, the α- dominant skyline satisfies requirements R 2. In this paper, we present WS-Sky (Web Service Skyline), an efficient and flexible Web service selection framework that implement the above mentioned skyline variants. We demonstrate that our framework assists users to find the most relevant Web services in a flexible way, and allows users to control the size of the retrieved Web services. Outline. The rest of the paper is structured as follows. Section II reviews related work. Section III describes our framework including the user interface, the skyline variants and the system architecture. Section IV presents our experimental study. Section V discusses some lessons learned in this research. Finally, Section VI concludes the paper. II. RELATED WORK During the last years, the problem of QoS-based Web service selection has received a lot of attention in the service computing community. In [9], the authors propose an extensible QoS computation model that supports an open and fair management of QoS data by incorporating user feedback. In [], [9], Zeng et al. propose a general and extensible model to evaluate QoS of both elementary and composite services. The authors use linear programming techniques to find the optimal selection of component services. Similarly, Ardagna et al. [2] extend the linear programming model to include local constraints. In [3], the authors studied the problem of service selection with multiple QoS constraints. The authors propose two models for the QoS-based service composition problem: a combinatorial model and a graph model. A heuristic algorithm is introduced for each model. Wang et al. [4] introduce QoS-based selection of semantic web services, the authors present a QoS ontology and selection algorithm to evaluate multiple qualities. However, these approaches require users to assign weights to QoS attributes. They thus suffer from lack of flexibility. Skyline analysis, which came from old research topics like contour problem [5], maximum vectors [6] and convex hull [7], was introduced into database domain by Börzsönyi et al. [8], which develop three algorithms: BNL, D&C and B-tree. Since, several algorithms have been developed to compute the skyline. They can be categorized into nonindex-based algorithms; e.g., SFS [9], LESS [20], SaLSa [2] and OSP [22], and index-based algorithms; e.g., Index [23], NN [24], BBS [25] and ZSearch [26]. However, computing the skyline Web services based on QoS is not practical, as (i) the size of the skyline is often very large and (ii) the skyline privileges Web services with a bad compromise between QoS attributes; thus the full skyline is not always informative. To address the first problem (i.e., the large size of the skyline), selecting on the basis of skyline variants has been investigated in the database community [29], [30], [27], [28], and recently in the service computing community [6], [8]. In [27], Lin et al. propose the top-k representative skyline, so that the k skyline points with the maximal number of dominated points can be produced. However, this approach often return similar points [28]. For diversifying the result, Tao et al. propose in [28] the distance based representative skyline. A similar approach is adapted in [6] for selecting Web services based on QoS. However, these approaches are only useful with anti-correlated datasets [6]. In [29], the authors propose the top-k skyline frequency. The skyline frequency of a point p is the number of subspaces where p is a skyline point. However, the exact algorithm results in a high computational cost as it needs to compute the skyline for each subspace. In [30], Chan et al. relax the notion of (Pareto) dominance to k-dominance, so that more of points are dominated. A point p is said to k-dominate another point q if and only if there are k dimensions in which p dominates q. The k-dominant skyline consists of the subset of points that are not k-dominated. However, the k-dominant skyline often returns an empty set. For instance, in our example, if k<4 the k-dominant skyline is empty. Specifically, we have s i is k-dominated by s i+ for i [, 5] and s 6 is k-dominated by s. Thus the k-dominant skyline can only return the skyline (with k =4). In addition, these approaches do not address the second problem (i.e., the privilege of Web services with a bad compromise between QoS attributes). In [8], we propose the α-dominant skyline, satisfying requirements R 2, i.e., privileging Web services with a good compromise between QoS parameters. In this work, we propose an unified framework, satisfying both R and R

3 Figure : A screenshot of the WS-Sky user interface. III. FRAMEWORK OVERVIEW In this section, we describe WS-Sky, including the user interface, our skyline variants and the system architecture. A. User Interface Figure presents a screenshot of the WS-Sky user interface. This interface was implemented with Java Swing. The user interacts with the system by issuing a selection query. More specifically, the query consists of a keyword which represents the functionality of the desired Web service (e.g., Search, News, etc.) as well as a set of desired QoS parameters. The user also chooses one of the skyline variants described in Section III-B (i.e., σ-dominant skyline or α- dominant skyline). WS-Sky evaluates the query, selects the most relevant Web services using the process described in Section III-C and returns the result set to the user. The result consists of the QoS of the retrieved Web services as well as their WSDL addresses as shown in the bottom of Figure. B. Skyline Variants At the heart of WS-Sky lie two skyline variants, the σ-dominant skyline (the quantitative variant) and the α- dominant skyline (the qualitative variant). We describe these skyline variants below. Given a set of d QoS parameters Q = {q,...,q d } and a set of functionally similar Web services S = {s,...,s n }. We use b(s i,s j ), w(s i,s j ), e(s i,s j ) to denote the number of QoS parameters where s i is better than, worse than and equal to s j, respectively. We also use q k (s i ) to denote the k th QoS value of s i. ) σ-dominant skyline: Consider Web services s 3 and s 4 in our example. With Pareto order, s 3 and s 4 are incomparable, i.e., neither s 3 dominates s 4 nor s 4 dominates s 3. However, s 4 is better than s 3 in three QoS parameters (q, q 3 and q 4 ), while s 3 is better than s 4 in only one QoS parameter (q 2 ). To overcome this limitation, we defined the notion of σ-dominance as follows: Definition. (σ-dominance) A Web service s i σ-dominates another Web service s j if b(s and only if e(s i,s j ) d and i,s j) d+w(s i,s j) e(s i,s j) σ. With σ [0, ]. Let us now reconsider Web services s 3 and s 4. With σ = 0.6, wehaves dominates s 3 and s 3 not 0.6-dominates s 4. This is more significant than s 3 and s 4 are incomparable provided by Pareto dominance. We can now define the σ- dominant skyline in the following way: Definition 2. (σ-dominant skyline) The σ-dominant skyline of S, denoted by σ-sky S, comprises the set of Web services in S that are not σ-dominated by any other Web service in S. In our example, with σ =0.6, the 0.6-dominant skyline comprises Web services s 2, s 4, s 5 and s 6. This instance shows that the σ-dominant skyline satisfies requirement R recall that the skyline comprises Web services s, s 2, s 3, s 4, s 5 and s 6. Theorem. If σ <σ, then σ -sky S σ-sky S. Proof: Assume that there exists a Web service s i, such that s i σ -sky S and s i / σ-sky S. Since s i / σ-sky S, there must exist a Web service s j such that s j σ-dominates b(s s i, i.e., j,s i) d+w(s σ. Thus, b(s j,s i) j,s i) e(s j,s i) d+w(s j,s i) e(s j,s i) σ as σ <σ. Hence, s j σ -dominates s i. Which leads to a contradiction as s i σ -sky S. From Theorem, we can see that the size of the σ- dominant skyline decreases with the decrease of σ and vice versa. Thus, the σ-dominant skyline allows users to control the size of the returned Web services. Remark. Note that, when σ =, σ-dominance is equivalent to Pareto dominance. Thus, the σ-dominant skyline is equivalent to the (Pareto) skyline. This means that our σ- dominant skyline can also return the (Pareto) skyline. 2) α-dominant skyline: To allow for an uniform measurement of Web service qualities independent of units, we normalize the different QoS values in the range [0, ], such that the lower the value, the higher the quality, as follows: For negative QoS parameters, i.e., the higher the value, the lower the quality (e.g., response time, latency, etc): N qk (s i) = q k(s i) min qk max qk min qk. For positive QoS parameters, i.e., the higher the value, the higher the quality (e.g., availability, reliability, etc.): N qk (s i) = maxq k q k(s i) max qk min qk. Where N qk (s i) is the normalized QoS value of the Web service s i on the QoS parameter q k and min qk (resp. max qk ) is the minimum (resp. maximum) value of the QoS parameter q k. Table II shows the QoS values of Web services in Table I after normalization

4 Table II: Web Services with Normalized QoS Values Web service q q 2 q 3 q 4 s s s s s s s s s s Theorem 2, shows that the size of the α-dominant skyline decreases with the decrease of α and vice versa. Thus, the α-dominant skyline also allows users to control the size of the returned Web services. Remark. It is worth to note that the α-dominant skyline can be applied when QoS values are not normalized. However, users are required to assign a specific ε and λ for each QoS parameter. This is a rather demanding task. For this purpose, our system normalizes the different QoS values in the range [0, ] as described above. Consider Web services s 4 and s 6 in our example. Applying Pareto dominance, we have neither s 4 dominates s 6 nor s 6 dominates s 4 because s 4 is better than s 6 in q 3 and q 4, and s 6 is better than s 4 in q and q 2. However, the QoS values where s 6 is better than s 4 are very closed, i.e., s 6 (q )=0 is closed to s 4 (q ) = 0.7 and s 6 (q 2 ) = 0.6 is closed to s 4 (q 2 )=0.8, while, s 4 (q 4 )=0is much smaller than s 6 (q 4 )=0.67 even if s 4 (q 3 ) is closed to s 6 (q 3 ). Motivated by this inadequacy, we define the notion of α-dominance as: Definition 3. (α-dominance) A Web service s i α-dominates another Web service s j if and d k= only if μ ε,λ(q k (s i),q k (s j)) d α. With α [0, ]. Where, μ ε,λ is a monotone comparison function that expresses the extent to which q k (s i ) is (more or less) strongly greater than q k (s j ). μ ε,λ is defined as: 0 ify x ε μ ε,λ (x, y) = ify x λ + ε y x ε λ otherwise Where ε 0 and λ>0. For more details, see [8]. Let us now reconsider Web services s 4 and s 6. With ε =0.02, λ =0.2 and α =0.5 we have s dominates s 6 and s 6 not 0.5-dominates s 4. Like σ-dominance, α- dominance is also more significant than Pareto dominance. The α-dominant skyline is defined in the following way: Definition 4. (α-dominant skyline) The α-dominant skyline of S in the context μ ε,λ, denoted by α-skyμ S ε,λ, comprises the set of Web services in S that are not α-dominated by any other Web service in S. In our example, with ε =0.02, λ =0.2 and α =0.7, the 0.7-dominant skyline comprises Web services s 2, s 4, s 5, s 6, s 7 and s 8. One can see that, Web services s and s 3, which are with a bad compromise between QoS parameters are discarded from the result, and Web services s 7 and s 8 which are (moderately) good in all QoS parameters are included. This instance shows that the α-dominant skyline satisfies requirement R 2. Theorem 2. If α <α, then α -skyμ S ε,λ α-skyμ S ε,λ. Proof: In a similar way as Theorem. Figure 2: WS-Sky Architecture. C. System Architecture WS-Sky was implemented in Java. Figure 2 describes the Architecture of WS-Sky. The main components of our system are the Service Annotator, the Service Locator and the Service Selector. The service registry is external and is accessed by our system at query time. The Service Annotator allows service providers to annotate WSDL description files of their Web services with (i) a tag representing the functionality of the Web service (e.g., Search, Weather forecast, etc.) and (ii) the different QoS values of the Web service. The Service Locator receives the keyword of the user query and communicates with the service registry in order 4 49

5 to select a set of candidate Web services that match the user query, i.e., their tags match the keyword of the user query. The Service Locator then sends the retrieved Web services (functionally similar Web services) to the Service Selector. The role of the Service Selector is to select the most relevant Web services based on QoS. The Service Selector consists of two main modules : the σ-dominant skyline and the α-dominant skyline. The α-dominant skyline uses the QoS Normalizer to normalize the QoS values in the way describes in the previous Section. At the beginning the Service Selector receives the user s desired skyline variant and its parameters (i.e., σ or α, ε and λ) and wait for a set of candidate Web services from the Service Locator. When receiving the candidate Web services, it computes the desired skyline variant using the different modules mentioned above and returns the result to the user. IV. EXPERIMENTAL EVALUATION In this section, we present an experimental evaluation of our approach, focusing on the size of the σ-dominant skyline and that of the α-dominant skyline. We also compute the size of the skyline for comparison purpose. In our evaluation, we experimented with both real-world and synthetic datasets. A. On Real-world Dataset We used the publicly available dataset QWS 2, which comprises measurement of nine QoS parameters for 2507 real-world Web services. These Web services were collected from public sources on the Web, and their QoS values were measured using commercial benchmark tools. More details about this dataset can bee found in [5]. For our experimental evaluation, we tagged each Web service by its functionality. For the purpose of our evaluation on real-life dataset, we considered two types of scenarios: (i) when a high number of QoS attributes is considered and (ii) when a low number of QoS attributes is considered. Table III: Size of the skyline variants ( Q =9) σ σ-sky α α-sky ) On a high number of QoS attributes: Table III shows the size of our skyline variants on different values of σ and α (ε =0.02 and λ =0.2 for the α-dominant skyline) for the keyword Search when considering nine QoS parameters. In this case, the skyline comprises 50 Web services, i.e., the size of the skyline is very large. Thus users will be overwhelmed during the selection process if the skyline is considered. From Table III, we can see that the size of the σ-dominant skyline and that of the α-dominant skyline increase with the increase of σ and α respectively. This 2 qmahmoud/qws/dataset/qws Dataset v2.txt conforms with both Theorem and Theorem 2. We can also see that in this scenario, i.e., when a high number of QoS parameters is considered, the σ-dominant skyline is more interesting than the α-dominant skyline. This is because, the size of the skyline is very large, and the σ-dominant skyline further reduces the size of the skyline, while the α-dominant skyline returns a large number of Web services for α>0.5. Table IV: Size of the skyline variants ( Q =5) σ σ-sky α α-sky ) On a low number of QoS attributes: Table IV shows the size of our skyline variants on different values of σ and α (ε =0.02 and λ =0.2 for the α-dominant skyline) for the keyword Search when considering five QoS parameters. In this case, the skyline consists of 7 Web services, i.e., the size of the skyline is quite small. From Table IV, we can also see that the size of the σ-dominant skyline and that of the α-dominant skyline increase with the increase of σ and α respectively. In addition, as shown in Table IV, the α-dominant skyline is more robust than the σ-dominant skyline. This is because, the size of the skyline is quite small, and the α-dominant skyline include somme interesting Web service (i.e., Web services with a good compromise between QoS parameters), while the size of the σ-dominant skyline is very small for σ<0.7. B. On Synthetic Datasets We used a publicly available synthetic generator 3 to obtain different datasets, varying three parameters: cardinality, dimensionality and distribution. The cardinality is the number of synthetic Web services in the dataset, while the dimensionality is the number of QoS attributes of each Web service in the dataset. There are three distributions in the synthetic datasets: correlated, independent and anticorrelated. In the correlated datasets, the QoS values of each Web service is positively correlated, i.e., a good value in some QoS attribute increases the possibility of a good value in the others. In the independent datasets, QoS values are independent each other. In the anti-correlated datasets, the QoS values of each Web service are negatively correlated, i.e., good values (or bad values) in all QoS attributes are less likely to occur. Table V lists the used notation ) Effect of Cardinality: Figure 3 shows that the size of σ-sky is insensitive to n at all. this is because as n varies more Web services may have chances not to be σ- dominated on the one hand, and more Web services have possibilities to be σ-dominated on the other hand. However, the size of α-sky follows similar trends to that of Sky for the

6 Cardinality [K] Cardinality [K] Cardinality [K] (a) Correlated (b) Independent (c) Anti-correlated Figure 3: Size vs n (d =6, σ =0.6, α =0.6) Dimensionality Dimensionality Dimensionality (a) Correlated (b) Independent (c) Anti-correlated Figure 4: Size vs d (n =6K, σ =0.6, α =0.6) Threshold Threshold Threshold (a) Correlated (b) Independent (c) Anti-correlated Figure 5: Size vs σ and α (n =6K, d =6). Table V: The summary of notation Notation Description Default n Cardinality 6K d Dimensionality 6 σ σ threshold 0.6 α α threshold 0.6 σ-sky σ-dominant skyline N/A α-sky α-dominant skyline N/A Sky Skyline N/A correlated and the independent datasets, i.e., it increases with the increase of n, while the size of α-sky is insensitive to n for the anti-correlated dataset and is very smaller than the size of Sky. As the anti-correlated dataset comprises a large number of Web services with a bad compromise between QoS attributes, they are thus excluded from the result. In contrast to the anti-correlated dataset, the size of α-sky is larger than that of Sky for the correlated dataset, as this last comprises a large number of Web services with a good compromise between QoS attributes, they are thus included in the result. For the independent dataset, the size of α-sky slightly smaller than that of Sky as some Web services (Web 43 5

7 service with a good compromise between QoS attributes) are included in the result on the one hand, and more Web services (Web service with a bad compromise between QoS attributes) are excluded from the result. Contrary to α-sky, the size of σ-sky is always smaller than the size of Sky as σ-sky is a subset of Sky. From this set of experiments, we can see that both σ-sky and Sky are more interesting for the correlated dataset as the α-sky returns a large number of Web services even if most of then are with a good compromise between QoS attributes, while for the independent dataset, σ-sky is more interesting. For the anti-correlated dataset, both σ-sky and α-sky are relevant, however, α-sky is more robust as it removes Web service with a bad compromise between QoS attributes. 2) Effect of Dimensionality: Figure 4 shows that in contrast to Sky whose the size increases significantly as d increases, d has no obvious effect on the size of σ-sky and the size of α-sky. This is related to the definition of each dominance variant, i.e., the (Pareto) dominance, the σ-dominance and the α-dominance relationships. Indeed, unlike σ-sky and α-sky, for Sky, when d increases, a Web service has better opportunity not to be dominated in all QoS attributes. Comparing the size of σ-sky and that of Sky, we can also observe that the size of σ-sky is constantly smaller than the size of Sky since σ-sky is a subset of Sky. In contrast to σ- Sky, the size of α-sky is larger than that of Sky for the correlated dataset. Since the correlated dataset comprises a large number of Web services with a good compromise between QoS attributes, they are thus included in the result. However for the independent and correlated datasets, the size of α-sky is larger than that of Sky when d is small, while smaller otherwise. Since when d is small, Web services with a good compromise between QoS attributes are more likely to occur for the independent dataset, they thus included in the result, while, for the anti-correlated dataset Web services with a good compromise between QoS attributes are less likely to occur, thus there are not enough Web services with a good compromise between QoS attributes to α-dominate others (i.e., those with a bad compromise between QoS attributes). From the results of this set of experiments, we can see that σ-sky is more interesting for both correlated and independent datasets, while both σ-sky and α-sky are interesting for the anti-correlated dataset. But, α-sky is more robust (except for d =2). 3) Effect of σ and α Thresholds: σ and α thresholds have a significant effect on the size of σ-sky and the size of α- Sky, respectively as shown is Figure 5 (the size of Sky does not change as it is not related to σ or α). This is because σ -Sky is a subset of σ-sky, if σ <σon the one hand (Theorem ), and α -Sky is a subset of α-sky, if α <αon the other hand (Theorem 2). From the results of this set of experiments, we can see that the right values of σ and α depends on the dataset. On the correlated dataset, for σ-sky, we must choose σ 0.6, since for σ<0.6, σ-sky returns an empty set, while for α-sky, α 0.6 is more interesting, since if σ>0.6, α-sky returns a large number of Web services. This is similar to the independent dataset. However, on the anti correlated dataset, we must choose 0.6 σ 0.7 for σ-sky, and α =0.6 for α-sky. V. LESSONS LEARNED In this section, we present some lessons learned from the use of our proposed system (i.e., WS-Sky) for selecting Web services based on QoS parameters. From the analysis in Section III-B and the results of our experiments on both real-world and synthetically generated datasets, we can observe that WS-Sky is (i) efficient since it assists user to find the most relevant Web services, and (ii) flexible since it allows users to control the size of the retrieved Web services by increasing and decreasing σ and α for the σ-dominant skyline and the α-dominant skyline, respectively. In other words, WS-Sky can support feedback from users. In addition, we can observe that the choice of the σ- dominant skyline or the α-dominant skyline depends on the Web services dataset. For example, on real-world dataset, we can see that the σ-dominant skyline is more robust than the α-dominant skyline on high number of QoS attributes, while the α-dominant skyline is more interesting on low number of QoS attributes but this is not valid for d = 2 (i.e., a low number of QoS attributes) on the synthetic datasets. This is related to (i) the five considered QoS attributes, i.e., if we considered five other QoS parameters, we can obtain different results, and (ii) the considered keyword, i.e., if we considered another keyword (different to Search ) we can also obtain different results. From these observations, we can see that the σ-dominant skyline and the α-dominant skyline are complementary, and alternative to the traditional skyline, since they satisfy respectively requirements R and R 2, and the σ-dominant skyline can also return the skyline (see Section III-B). VI. CONCLUSION In this paper, we have addressed the problem of Web service selection based on QoS and proposed WS-Sky, an efficient and flexible Web service selection framework, which leverages two skyline variants alternative to the traditional skyline. We have demonstrated that WS-Sky helps users select the most relevant Web services in a flexible way. An interesting future direction is to develop modules for collecting Web services from the Internet, and integrate them into WS-Sky to improve it

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