The proof of subgraph isomorphism being NP-complete is simple and based on reduction of the clique problem , an NP-complete decision problem in which the input is a single graph G and a number k, and the question is whether G contains a complete subgraph with k vertices. To translate this to a subgraph isomorphism problem, simply let H be the complete graph Kk; then the answer to the subgraph isomorphism problem for G and H is equal to the answer to the clique problem for G and k. Since the clique problem is NP-complete, this polynomial-time many-one reduction shows that subgraph isomorphism is also NP-complete. Because the Hamiltonian cycle problem is NP-complete even for planar graphs , this shows that subgraph isomorphism remains NP-complete even in the planar case. However the complexity-theoretic status of graph isomorphism remains an open question. Although its running time is, in general, exponential, it takes polynomial time for any fixed choice of H with a polynomial that depends on the choice of H.
Horoscope Matching by Ashok Prajapati
Advertisement Every day, millions of single adults, worldwide, visit an online dating site. Many are lucky, finding life-long love or at least some exciting escapades. Others are not so lucky. The industry—eHarmony, Match, OkCupid, and a thousand other online dating sites—wants singles and the general public to believe that seeking a partner through their site is not just an alternative way to traditional venues for finding a partner, but a superior way.
Matchmaking Semantic Based for Information System Interoperability I Wayan Simri Wicaksana1* 1 Gunadarma University, Jalan Margonda Raya , Depok, , Indonesia Unlike the traditional model of information pull, matchmaking is base on a cooperative partnership between information.
UDDI provides limited search facilities allowing only a keyword-based search of businesses, services, and the so called tModels based on names and identifiers. This category-based keyword-browsing method is clearly insufficient. This improvement enables software agents or search engines to automatically find appropriate Web services via ontologies and reasoning algorithm enriched methods.
However, the high cost of formally defining to the heavy and complicated services makes this improvement widespread adoption unlikely. To cope with these limitations, we have developed a suite of methods which assesses the similarity of Web services to achieve matchmaking. In particular, we present a conceptual model which classifies properties of Web services into four categories.
For each category, a similarity assessment method has been given. In Web service matchmaking process, these similarity assessment methods can be used together or individually. Experiments highlight complementary contributions that our work makes to facilitate web service matchmaking.
The Google file system
Introduction to the Special Issue: The promise of the Semantic Web is to make machine understandable all the information available on the Web. The knowledge on any specific domain can be stored in an explicit and reusable format by means of ontology languages. Moreover, exploiting the formal semantics of ontology languages, implicit knowledge can be elicited through automated reasoning mechanisms.
Semantic Web technologies open new scenarios and suggest new approaches to classical problems.
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Clustering-Based Semantic Web Service Matchmaking with Automated Knowledge Acquisition
To fully fulfill the modularity and loosely coupled characteristics of P2P semantic mapping paradigm proposed in our previous work, a mapping creation method based on semantic discovery is presented to avoid a time-consuming and labor-intensive artificial mapping creation process. This method creates semantic mapping between peer node models by establishing the semantic relations between elements from different peer node models. Semantic relative candidates are captured through the correspondence semantic matching process including concepts matching process, attributes matching process, relations matching process, concepts and relations matching process, and concepts and attributes matching process.
Abstract. Several approaches have been proposed to deal with the web service matchmaking problem. Unfortunately, most of these solutions are purely syntactic measures based on the input/output interface specifications of web services and consequently lake accuracy.
I claimed myself quite selective and have fussy in choosing a life partner! During younger time, I was a workaholic and ambitious businessman, no time for relationship. One day I was approached by iMatch, I was overwhelmed by the fast and accurate response in delivering quality matching service from iMatch. There were a few introductions with few weeks, the only one who really caught my eye, who came with the charm to catch my heart; she immediately grabbed my attention with her witty profile and captivating eyes.
I am happy to say those few months later, I still wake up every morning feeling like the luckiest man in the world with Vivian around me. We just got registered September and wedding will be held next year. The candidates are mostly of pleasant good quality. We are discussing for children now. Christine still keep good rapport with me although my file had been closed, I more than willing and happy to share with my other single friends by telling our experience of successful match in iMatch.
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Web Service Matchmaking by Subgraph Matching
Vento , ” A recent paper posed the question: What are we really talking about?
Improving Performance of Web Services Query Matchmaking with Automated Knowledge Acquisition Chaitali Gupta, Rajdeep Bhowmik, Michael R. .
Graphs are widely used to model complicated data semantics in many applications. In this paper, we aim to develop efficient techniques to retrieve graphs, containing a given query graph, from a large set of graphs. Considering the problem of testing subgraph isomorphism is generally NP-hard, most of Considering the problem of testing subgraph isomorphism is generally NP-hard, most of the existing techniques are based on the framework of filtering-and-verification to reduce the precise computation costs; consequently various novel feature-based indexes have been developed.
While the existing techniques work well for small query graphs, the verification phase becomes a bottleneck when the query graph size increases. Motivated by this, in the paper we firstly propose a novel and efficient algorithm for testing subgraph isomorphism, QuickSI. Secondly, we develop a new feature-based index technique to accommodate QuickSI in the filtering phase. Our extensive experiments on real and synthetic data demonstrate the efficiency and scalability of the proposed techniques, which significantly improve the existing techniques.
Show Context Citation Context Simple Graph Database NP-complete .
ECU Libraries Catalog
A critical step in the process of developing applications based on web services is service discovery, i. Discovery through catalog-style browsing such as supported currently by web-service registries is clearly insufficient. To support programmatic service discovery, we have developed a suite of methods that assess the similarity between two WSDL Web Service Description Language specifications based on the structure of their data types and operations and the semantics of their natural language descriptions and identifiers.
Read “Behavioral matchmaking for service retrieval: Application to conversation protocols, Information Systems” on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
The description of the behavior of the requested web service and the description of the behavior of the computing service conform to a description logic. Based on logical rules of the description logic, it is determined that the description of the behavior of the existing computing service is subsumed by the description of the behavior of the requested web service. For example, users may implement or access a software application to obtain a stock quote or an airline reservation, or to manage aspects of a business enterprise.
Particular functions of software applications may be implemented as more or less discrete components, and may thereby be used in a variety of contexts. For instance, the example just given of software for obtaining a stock quote may be implemented in a software component that may then be deployed into many other software applications, such as, for example, a stock price charting tool or a daily stock price reporting tool.
Such re-use of software application components may, among other advantages, increase the efficiency and reliability of the components themselves, and of other applications that make use of the components, as well as reducing software development and maintenance costs.
BPEL Processes Matchmaking for Service Discovery – CiteSeerX
The quantity and quality of the information, the process of highlighting the important factors, and the duration to process the decision are critical. Nowadays, outsource hiring and recruitment companies have played an important role in many industries, for reducing the search duration and increasing the likeliness of employing the right person. However, there are too many human resource HR companies in the market.
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If true, this would be a very interesting result. However your problem goes beyond asking for a maximum matching to one of these that minimizes the total ages of women in the matching. For this we could enumerate all possible maximum matchings and pick the one with the least total ages. See the Section below on methods for doing this, but the number of possible maximum matchings can grow exponentially.
Fortunately we can transform our requirement of minimizing the edge weights in a maximum matching in this specific problem to a requirement of maximizing the sum of edge weights. Now a maximum weight bipartite matching can be found by a “greedy algorithm” that exploits the matroid structure of these problems. Notes on enumerating all maximum matchings The literature on enumerating all maximum matchings is fairly mature.
The paper Finding All Allowed Edges in a Bipartite Graph by Tamir Tassa seems to give a good survey of the known literature on finding maximum matchings. The Introduction of this paper describes a matchmaking agency with a setup very similar to what is described in this Question.
A Graph-Based Approach to Web Service Matchmaking
Comment Software applications as web services which can be invoked remotely by users In this paper we argue that, in many situations, the service discovery process The capability to easily find useful services software applications, software components, scientific computations becomes increasingly critical in several fields. Recent works have demonstrated that this approach is not sufficient to discover relevant components.
Semantic Web services are generally considered as the evolution of conventional Web services. Semantic information included in the service descriptions enables the development of advanced matchmaking schemes, capable of assigning degrees of match to the discovered services. In this paper, we address issues related to the evaluation of the retrieval effectiveness of semantic matchmaking .
Get expert advice delivered right to your inbox We try to avoid the fluff and deliver tips and guides that really matter Email Thank you, you have successfully subscribed to our newsletter! Enjoy reading our tips and recommendations. What features matter most? How much does it cost? It is important to decide how much you can afford to pay before committing to a site. An online dating site might be free, or have different payment levels. If there are different levels of membership and payment, check to see which features are included in each level and which features you find essential.
The membership price might be a yearly, monthly, or quarterly amount. Recently, there has been many mobile dating apps that will let you pay by the action instead of by a set amount of time. It is important to read the fine print and to find out how easy or difficult it is to cancel a subscription. What is the matching process? Online dating websites offer a variety of methods on how to search for a mate.
STRUCTURAL AND SEMANTIC MATCHING FOR ASSESSING WEB-SERVICE SIMILARITY
This is a serious drawback in a fast growing Internet that is facing the challenge to deal with an increasing number of published services. The proposed solutions to cope with this limitation consider the process part of a service description as a graph in the similarity measure. This kind of solutions has better accuracy but suffer from high computational complexity because they rely on time consuming graph matching tools.
International audienceSeveral approaches have been proposed to deal with the web servicematchmaking problem. Unfortunately, most of these solutions are purely syntac-tic measures based on the input/output interface specifications of web servicesand consequently lake accuracy. This is a serious.
If you are having issues matching players, here is a quick checklist: Verify that you are using same AppId in all clients. Verify that clients are connected to the same Region. Only players connected to same region can play with each other no matter what device or platform they’re using. Verify that you are using same AppVersion in all clients. More information can be found here.
Verify that players have different UserIds. Players with same UserId cannot join the same room. Before trying to join a room by name, make sure that this room is created or use JoinOrCreateRoom. If you are trying to join a random room, make sure to choose the same lobby name and type used when creating it. If you are doing random matchmaking using room properties as a filter make sure to set the keys of those properties to be visible from the lobby when creating the room.
If you are doing random matchmaking with SQL filters make sure to set the reserved filtering properties keys used to be visible from the lobby. It is also important to relax the SQL filter with each random matchmaking attempt or create new rooms at some point after a number of failed attempts.