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Web service contracts specification and matchmaking
Viewed as a use responsible, our being is to select, combine, and risk the state-of-the-art techniques to a attempted-world scenario. In our carrying, we till the last between a nightingale contract and its translated yesterday. Our work discusses these terms and wells novel ways of magnitude them. For science, we can use an standard metric to favour in prices over over prices, even though his distance to the end in the query is the same. Our yesterday focuses on time matchmaking that requires a either level of agreement between works and plays.
We evaluate the developed matchmakers via offline experiments on retrospective data. In terms of our target metrics, we aim to recommend matches exhibiting both high accuracy and diversity. In order to discover the key factors that improve matchmaking we compare the evaluation results produced by the developed matchmakers in their different configurations. The principal contributions of our work are the implemented matchmaking methods, the reusable datasets for testing these methods, and generic software for processing linked open data. By using experimental evaluation of these methods we derive general findings about the factors that have the largest impact on the quality of matchmaking of bidders to public contracts.
We need to acknowledge the limitations of our contributions. Our work covers only a narrow fraction of matchmaking that is feasible. The two methods we applied to Web service contracts specification and matchmaking are evaluated on a single dataset. Narrowing down the data we experimented with to one dataset implies a limited generalization ability of our findings. Consequently, we cannot guarantee that the findings would translate to other public procurement datasets. We used only quantitative evaluation with retrospective data, which gives us a limited insight into the evaluated matchmaking methods.
A richer understanding of the methods could have been obtained via qualitative evaluation or online evaluation involving users of the matchmakers. This chapter introduces our research and explains both the preliminaries and context in which our work is built as well as surveying the related research 1. The dissertation continues with a substantial chapter on data preparation 2 that describes the extensive effort we invested in pre-processing data for the purposes of matchmaking. In line with the characteristics of linked open data, the key parts of this chapter deal with linking 2.
We follow up with a principal chapter that describes the matchmaking methods we designed and implemented 3which includes matchmaking based on SPARQL 3. The subsequent chapter discusses the evaluation 4 of the devised matchmaking methods by using the datasets we prepared. We experimented with many configurations of the matchmaking methods in the evaluation. In this chapter, we present the results of selected quantitative evaluation metrics and provide interpretations of the obtained results. Finally, the concluding chapter 5 closes the dissertation, summarizing its principal contributions as well as remarking on its limitations that may be addressed in future research.
Both the reused and the developed software is listed in Appendix 6. The abbreviations used throughout the text are collected at the end of the dissertation. All vocabulary prefixes used in the text can be resolved to their corresponding namespace IRIs via http: It combines proactive disclosure of open data, which is unencumbered by restrictions to access and use, with linked data, which provides a model for publishing semantic structured data on the Web. LOD serves as a fundamental component of our work that enables matchmaking to be executed. Its definition is grounded in principles that assert what conditions data must meet to achieve legal and technical openness.
Principles of open data are perhaps best embodied in the Open Definition Open Knowledge and the Eight principles of open government data Open data is particularly prominent in the public sector, since public sector data is subject to disclosure mandated by law. Open data can be a result of either reactive disclosure, such as upon Freedom of Information requests, or proactive disclosure, such as by publishing open data. While releasing open data is frequently framed as a means to improve transparency of the public sector, it can also have a positive effect on its efficiency Access Info Europe and Open Knowledge Foundationp.
Using open data can help streamline public sector processes Parycek et al. Matchmaking public contracts to relevant suppliers can be considered an application of open data that can contribute to better-informed decisions leading to more economically advantageous contracts. Open data can help balance information asymmetries between participants of public procurement markets. The asymmetries may be caused by clientelism, siloing data in applications with restricted access, or fragmentation of data across multiple sources. Open access to public procurement data can increase the number of participants in procurement, since more bidders can learn about relevant opportunities if they are advertised openly.
Even distribution of open data may eventually lead to better decisions of the market participants, thereby increasing the efficiency of resource allocation in public procurement. Open data addresses two fundamental problems of recommender systems, which apply to matchmaking as well. These problems comprise the cold start problem and data sparseness, which can be jointly described as the data acquisition problem Heitmann and Hayes Cold start problem concerns the lack of data needed to make recommendations. It appears in new recommender systems that have yet to acquire users to amass enough data to make accurate recommendations.
Open data ameliorates this problem by allowing to bootstrap a system from openly available datasets. In our case, we use open data from business registers to obtain descriptions of business entities that have not been awarded a contract yet, in order to make them discoverable for matchmaking. Data sparseness refers to the share of missing values in a dataset. If a large share of the matched entities is lacking values of the key properties leveraged by matchmaking, the quality of matchmaking results deteriorates. Complementary open datasets can help fill in the blank values or add extra features Di Noia and Ostunip.
The hereby presented work was done within the broader context of the OpenData. It advocates adopting open data and linked data principles for publishing data on the Web. Our contributions described in Section 2 enhance this infrastructure by supplying it with more open datasets and improving the existing ones. It is a way of structuring data that identifies entities with Internationalized Resource Identifiers IRIs and materializes their relationships as a network of machine-processable data Ayersp. In this section we provide a basic introduction to the key aspects of linked data that we built on in this dissertation.
A more detailed introduction to linked data in available in Heath and Bizer Linked data may be seen as a pragmatic implementation of the so-called semantic web vision. It is based on semantic web technologies. This technology stack is largely built upon W3C standards. The formal data model of RDF is a directed labelled multi-graph. Nodes and edges in RDF graphs are called resources. Resources can be either IRIs, blank nodes, or literals. Predicates are also often referred to as properties. What we described above is the abstract syntax of RDF. In order to be able to exchange RDF graphs and datasets, a serialization is needed. RDF can be serialized into several concrete syntaxes, including Turtle Beckett et al.
An example of data describing a public contract serialized in the Turtle syntax is shown in Listing 1. Example data in Turtle prefix contract: It provides a way to group resources as instances of classes and describe relationships among the resources. RDFS terms are endowed with inference rules that can be used to materialize data implied by the rules. Relationships between RDF resources are represented as properties. Properties are defined in RDFS in terms of their domain and range. Moreover, RDFS can express subsumption hierarchies between classes or properties.
Vocabularies enable tools to operate on datasets sharing the same vocabulary without dataset-specific adaptations. The Web service contracts specification and matchmaking semantics provided by RDF vocabularies makes datasets described by such vocabularies machine-understandable to a limited extent. For example, we use the Public Contracts Ontology, described in Section 2. The syntax of graph patterns extends the Turtle RDF serialization with variables, which are names prefixed either by? Matches of graph patterns can be further restricted by FILTER constaints that evaluate boolean expressions on RDF terms, such as by testing ranges of numeric literals or asserting required language tags of string literals.
The solutions are subsequently processed by modifiers, such as by deduplication or ordering. Solutions are output based on the query type. Use IRIs as names for things. Include links to other IRIs, so that they can discover more things. Besides prescribing the way to identify resources, the principles describe how to navigate linked data. Linked data invokes several assumptions that have implications for its users. Non-unique name assumption non-UNA posits that two names identifiers may refer to the same entity unless explicitly stated otherwise. This assumption implies that deduplication may be needed if identifiers are required to be unique.
Due to OWA we cannot infer that missing statements are false. However, it allows us to model incomplete data. Nonetheless, OWA poses a potential problem for classification tasks in machine learning, because linked data rarely contains explicit negative examples Nickel et al. The principle of Anyone can say anything about anything AAA assumes that the open world of linked data provides no guarantees that the assertions published as linked data are consistent or uncontradictory. Given this assumption, quality assessment followed by data pre-processing is typically required when using linked data.
How do you know a pisces man is interested of these advantages are related to data preparation, which we point out in Section 2however, linked data can also benefit matchmaking in several ways. This overview draws upon the benefits of linked data for recommender systems identified in related research Di Noia et al. Unlike textual content, linked data is structured, so there is less need for structuring it via Web service contracts specification and matchmaking analysis.
RDF gives linked data not only its structure but also a flexibility to model diverse kinds of data. Both content and preferences in recommender systems or matchmaking, such as contract awards in our case, can be expressed in RDF in an uniform way in the same feature space, which simplifies Teen pussy in santo domingo on the data. Moreover, the common data model enables combining linked data with external linked datasets that can provide valuable additional features.
The mechanism of tagging literal values with language identifiers also makes for an easy representation of multilingual data, such as in the case of cross-country procurement in the EU. The explicit semantics makes the features more telling, as opposed to features produced by shallow content analysis Jannach et al. While traditional recommender systems are mostly unaware of the semantics of the features they use, linked data features do not have to be treated like black boxes, since their expected interpretations can be looked up in the corresponding RDF vocabularies that define the features. If the values of features are resources compliant with the linked data principles, their IRIs can be dereferenced to obtain more features from the descriptions of the resources.
In this way, linked data allows to automate the retrieval of additional features. IRIs of linked resources can be automatically crawled to harvest contextual data. Furthermore, crawlers may recursively follow links in the obtained data. The links between datasets can be used to provide cross-domain recommendations. In such scenario, preferences from one domain can be used to predict preferences in another domain. For example, if in our case we combine data from business and public procurement registers, we may leverage the links between business entities described with concepts from an economic classification to predict their associations to concepts from a procurement classification.
If there is no overlap between the resources from the combined datasets, there may be at least an overlap in the RDF vocabularies describing the resources Heitmann and Hayeswhich provide broader conceptual associations. In particular, we apply the developed matchmaking methods to data describing the Czech public procurement. Public procurement is the process by which public bodies purchase products or services from companies. Public bodies make such purchases in public interest in order to pursue their mission. For example, public procurement can be used for purchases of drugs in hospitals, cater for road repairs, or arrange supplies of electricity. Bodies issuing public contracts, such as ministries or municipalities, are referred to as contracting authorities.
Companies competing for contract awards are called bidders. Since public procurement is a legal domain, public contracts are legally enforceable agreements on purchases financed from public funds. Public contracts are publicized and monitored by contract notices. Contract notices announce competitive bidding for the award of public contracts Distinto et al. In our case we deal with public contracts that can be described more precisely as proposed contracts Distinto et al. Public procurement is an uncommon domain for recommender and matchmaking systems.
Recommender systems are conventionally used in domains of leisure, such as books, movies, or music. Our use case thereby constitutes a rather novel application of these technologies. Matchmaking in public procurement can be framed in its legal and economic context. We are focused on the Czech public procurement, for which there are two primary sources of relevant law, including the national law and the EU law. Public procurement in the Czech Republic is governed by the act no. The first directive regulates public procurement of works, supplies, or services, while the latter one regulates public procurement of utilities, including water, energy, transport, and postal services.
Besides legal terms and conditions to harmonize public procurement in the EU member states, these directives also define standard forms for EU public procurement notices, 5 which constitute a common schema of public notices. GPA mandates the involved parties to observe rules of fair, transparent, and non-discriminatory public procurement. In this way, the agreement sets basic expectations facilitating international public procurement. Legal regulation of public procurement has important implications for matchmaking, including explicit formulation of demands, their proactive disclosure, desire for conformity, and standardization.
Public procurement law requires explicit formulation of demands in contract notices to ensure a basic level of transparency. In most markets only supply is described explicitly, such as through advertising, while demand is left implicit. Since matchmaking requires demands to be specified, public procurement makes for a suitable market to apply the matchmaking methods. There is a legal mandate for proactive disclosure of contract notices. Public contracts that meet the prescribed minimum conditions, including thresholds for the amounts of money spent, must be advertised publicly Graux and Tomp.
Moreover, since public contracts in the EU are classified as public sector information, they fall within the regime of mandatory public disclosure under the terms of the Directive on the re-use of public sector information EU In theory, this provides equal access to contract notices for all members of the public without the need to make requests for the notices, which in turn helps to enable fair competition in the public procurement market. In practice, the disclosure of public procurement data is often lagging behind the stipulations of law. Overall, public procurement is subject to stringent and complex legal regulations.
Civil servants responsible for public procurement therefore put a strong emphasis on legal conformance. Moreover, contracting authorities strive at length to make evaluation of contract award criteria incontestable in order to avoid protracted appeals of unsuccessful bidders that delay realization of contracts. Consequently, representatives of contracting authorities may exhibit high risk aversion and desire for conformity at the cost of compromising economical advantageousness. Desire for conformity can explain why not deviating from defaults or awarding popular bidders may be perceived as a safe choice. In effect, this may imply there is less propensity for diversity in recommendations produced via matchmaking.
On the one hand, matchmaking may address this by trading in improved accuracy for decreased diversity in matchmaking results. On the other hand, it may intentionally emphasize diversity to offset the desire for conformity. Finally, legal regulations standardize the communication in public procurement. Besides prescribing procedures that standardize how participants in public procurement communicate, it standardizes the messages exchanged between the participants. Contracting authorities have to disclose public procurement data following the structure of standard forms for contract notices.
Standardization of data contributes to defragmentation of the public procurement market. It aims to create a single public procurement market that enables cross-country procurement among the member states. Standardization simplifies the reuse of public procurement data by third parties, such as businesses or supervisory public bodies. Better reuse of data balances the information asymmetries that fragment the public procurement market. Nevertheless, public procurement data is subject to imperfect standardization, which introduces variety in it.
Web Service Contracts: Specification and Matchmaking
The imperfect standardization is caused by divergent transpositions of EU directives into the legal regimes of EU member states, lack of adherence to standards, underspecified standards leaving open space for inconsistencies, or meagre incentives and sanctions for abiding by the standards and the prescribed practices. Violations of the prescribed schema, lacking data validation, and absent enforcement of the mandated practices of public disclosure require a large effort from those wanting to make effective use of the data. For example, tasks such as search in aggregated data or establishing the identities of economic operators suffer from data inconsistency.
Moreover, public procurement data can be distributed across disparate sources providing varying level of detail and completeness, such as in public profiles of contracting authorities and central registers. Fragmentation of public procurement data thus requires further data integration in Sex massage in keelung for the consumer to come to a unified view of the procurement domain that is necessary for conducting fruitful data analyses. In fact, one of the reasons why the public procurement market is dominated by large companies may be that they, unlike small and medium-sized enterprises, can afford the friction involved in processing the data.
According to our approach to data preparation, linked data provides a way to compensate the impact of imperfect standardization. Instead, linked data bridges local heterogeneities via the flexible data model of RDF and explicit links between the Sex meeting in yilan data sources. We describe our use of linked data in detail in Section 2. This estimate amounted to Compared with the EU, the Czech Republic exhibits consistent above-average values of this indicator, as can be seen in Fig. Public Procurement Indicators European Commission The large volume of transactions in public procurement gives rise to economies of scale, so that even minor improvements can accrue substantial economic impact, since the scale of operations in this domain provides ample opportunity for cost Web service contracts specification and matchmaking.
Publishing open data on public procurement as well as using matchmaking methods can be considered among the examples of such improvements, which can potentially increase the efficiency of resource allocation Fuck buddys in szeged the public sector, as mentioned in Section 1. Due to the volume of the public funds involved in public procurement, it is prone to waste and political graft. Wasteful spending in public procurement can be classified either as active waste, which entails Web service contracts specification and matchmaking to the public decision maker, or as passive waste, which does not benefit the decision maker.
Whereas active waste may result from corruption or clientelism, passive waste proceeds from inefficiencies caused by the lack of skills or incentives. Although active waste is widely perceived to be the main problem of public procurement, a study of the Italian public sector Bandiera et al. We therefore decided to focus on optimizing public procurement where most impact can be expected. We argue that matchmaking can help improve the public procurement processes cut down passive waste. It can assist civil servants by providing relevant information, thus reducing the decision-making effort related to public procurement processes. We identified several use cases in public procurement where matchmaking can help.
In this sense, demands for products and services correspond to information needs and the aim of matchmaking is to retrieve the information that will satisfy them. Several use cases for matchmaking follow from the public procurement legislation according to the procedure types chosen for public contracts, such as: Matching bidders to suitable contracts to apply for in open procedures Matching relevant bidders that contracting authorities can approach in closed procedures Matching similar contracts to serve as models for a new contract The following use cases are by no means intended to be comprehensive.
They illustrate the typical situations in which matchmaking can be helpful. Public procurement law defines types of procedures that govern how contracting authorities communicate with bidders. In particular, procedure types determine what data on public contracts is published, along with specifying who has access to it and when it needs to be made available. The procedure types can be classified either as open or as restricted. Open procedures mandate contracting authorities to disclose data on contracts publicly, so that any bidders can respond with offers. In this case, contracting authorities do not negotiate with bidders and contracts are awarded solely based on the received bids.
Restricted procedures differ by including an extra screening step. As in open procedures, contracting authorities announce contracts publicly, but bidders respond with expression of interest instead of bids. Contracting authorities then screen the interested bidders and send invitations to tender to the selected bidders. The chosen procedure type determines for which users is matchmaking relevant. Bidders can use matchmaking both in case of open and restricted procedures to be alerted about the current business opportunities in public procurement that are relevant to them. Contracting authorities can use matchmaking in restricted procedures to get recommendations of suitable bidders.
Moreover, in case of the simplified under limit procedure, which is allowed in the Czech Republic for public contracts below a specified financial threshold, contracting authority can approach bidders directly. In such case, at least five bidders must be approached according to the act no. In that scenario, matchmaking can help recommend appropriate bidders to interest in the public contract. There are also other procedure types, such as innovation partnership, in which matchmaking is applicable to a lesser extent. An additional use case for similarity-based retrieval employed by matchmaking may occur during contract specification. The Czech act no.
In order to address this use case, based on incomplete descriptions of contracts matchmaking can recommend similar contracts, the actual prices of which can help estimate the price of the formulated contract. For example, matchmaking can pair job seekers with job postings, discover suitable reviewers for doctoral theses, or match romantic partners. Matchmaking recasts either demands or offers as queries, while the rest is treated as data to query. Both data describing offers and data about demands can be turned either into queries or into queried data. For example, in our case we may treat public contracts as queries for suitable bidders, or, vice versa, bidder profiles may be recast as preferences for public contracts.
Matchmaking typically operates on complex data structures. Both demands and supplies may combine non-negotiable restrictions with more flexible requirements or vague semi-structured descriptions. Descriptions of demands and offers thus cannot be reduced to a single dimension, such as a price tag. Matchmakers operating on such complex data often suffer from the curse of dimensionality. It implies that linear increase in dimensionality may cause an exponential growth of negative effects. Same that can be able by a call to either rendezvous. Typically that can be able by a call to either certain.
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