Collaborative Networks

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Creating and managing value in collaborative networks

Creating and managing value in collaborative networks

The proposition underpinning the work presented in this paper is that value creation in collaborative organisations should be a win-win-win situation for all parties concerned. To this end, the purpose of this paper is to develop a better understanding of value creation and the role of value propositions in collaborative networks such as supply chains, extended/virtual enterprises and clusters. This is achieved by examining the theory behind value creation and value propositions in the context of different collaborative enterprises/networks. As a conclusion a comparison of different collaborative enterprises/networks is presented with particular emphasis on the different value transactions and their relationship with individual and collective capabilities and competencies.
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Collaborative Networks for Both Improvement and Research

Collaborative Networks for Both Improvement and Research

Moving signi fi cant therapeutic discoveries beyond early biomedical translation or T1 science and into practice involves: (1) T2 science, identifying “ the right treatment for the right patient in the right way at the right time ” (eg, patient-centered outcomes research) and tools to implement this knowledge (eg, guidelines, registries); and (2) T3 studies addressing how to achieve health care delivery change. Collaborative improvement networks can serve as large- scale, health system laboratories to engage clinicians, researchers, patients, and parents in testing approaches to translate research into practice. Improvement networks are of particular importance for pediatric T2 and T3 research, as evidence to establish safety and ef fi cacy of therapeutic interventions in children is often lacking. Net- works for improvement and research are also consistent with the Institute of Medicine ’ s Learning Healthcare Systems model in which learning networks provide a system for improving care and outcomes and generate new knowledge in near real-time. Creation of total population registries in collaborative network sites provides large, representative study samples with high-quality data that can be used to generate evidence and to inform clinical decision-making. Networks use collaboration, data, and quality-improvement methods to stan- dardize practice. Therefore, variation in outcomes due to unreliable and unnecessary care delivery is reduced, increasing statistical power, and allowing a consistent baseline from which to test new strategies. In addition, collaborative networks for improvement and research offer the opportunity to not only make improvements but also to study improvements to determine which interventions and combination of strategies work best in what settings. Pediatrics 2013;131:S210 – S214
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ENTREPRENEURIAL ORIENTATION, COLLABORATIVE NETWORKS, AND NONPROFIT PERFORMANCE

ENTREPRENEURIAL ORIENTATION, COLLABORATIVE NETWORKS, AND NONPROFIT PERFORMANCE

This study has limitations with respect to data collection and response burden. From the ethnographic interviews at the beginning of the study, it was discovered that practitioners would be more willing to complete the survey if it was simple, short, and intuitive. To do so, tradeoffs had to be made about what and what not to include in the survey instrument. In order to obtain data on alter-to-alter ties, the survey would have to have been much longer and burdensome. With the goal to maximize the response rate of a sample consisting of busy presidents, CEOs, and directors, the research team decided to focus solely on direct ties. This decision was made with logic that at higher levels of analysis (e.g. teams, departments, organizations, etc.), networks are often fuzzier, more loosely-coupled systems, with direct ties likely having a substantial effect on immediate outcomes for a focal actor. Hence, this present study did not theorize about the antecedents or consequences of the density of collaborative networks. This should be noted as a limitation since EDOs might perform better if their collaborative partners also have ties. One might expect that such higher levels of density would have a positive effect on performance. Ties between alters could make coordination easier for the successful implementation of projects that involve a shared objective. Or, such ties might also have a negative affect due to a greater likelihood of less nonredundant information and less control for the focal EDO (Burt, 2005). It could be that a greater density for an EDO leads to constraint, preventing an EDO from engaging in entrepreneurial and innovative activity that could lead to substantial gains in performance and/or effectiveness. Future work could explore such
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A review of virtuality and collaborative networks

A review of virtuality and collaborative networks

There is also a need to be able to predict which partners would facilitate successful collaboration. Partner assessment is important to determine the degree of suitability of potential partners which may be based on their past work in collaborative networks. A common focus in this assessment has been on ‘hard’ factors such as the matching of competencies but there is also a need to consider ‘soft factors’ such as the organisation’s character and research is needed to develop a full assessment model for collaboration readiness. The alignment of values can enable partner organisations to identify potential partners but no measures are available. Thus there is a need for the development of methodologies to measure the value system alignment in collaborative networks as a predictive indicator of collaboration.
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The Standardization of Supporting Tools: Advantage Competitive for Collaborative Networks

The Standardization of Supporting Tools: Advantage Competitive for Collaborative Networks

Traditionally, manufacturing companies, the gateway for information technology (IT) have been implementing business management tools, tools typically enterprise resource planning (ERP) focused exclusively on internal processes manufacturing and remaining largely isolated from the rest of the elements of the value chain (suppliers, customers, etc.). The objective of this paper is to propose action lines to solve the problems inherent in collaborative knowledge management related technological barrier by implementation project business management tools. As mist relevant contribution are both the search for standardization and the application of techniques in Project Management to try to achieve success in the implementation and establishing of collaborative networks.
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A metric for collaborative networks

A metric for collaborative networks

The objective of this paper is to provide a metric that could be used to define success in a collaborative network. The metric shows three kinds of measurements that might influence the success of collaborative networks. This paper contributes to the body of knowledge by developing a methodology for measuring partners’ contribution, involvement and outcome in the collaborative network as a system within IDEF0 functional modelling. The contribution measurement uses Analytical Hierarchy Process (AHP) approach to measure partners’ contribution. Likert scale is also applied to measure the health of the relationships based on key performance indicators of relationship attributes. Analytical with mathematical approach is employed to measure the partners’ outcome of the collaborative network.
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Relevant problems in collaborative processes of non-hierarchical manufacturing networks

Relevant problems in collaborative processes of non-hierarchical manufacturing networks

In collaborative networks context there is a wealth of knowledge available, but there is a need to consolidate this knowledge through developing a framework that provides models, guidelines and tools for supporting collaborative processes. Despite the interest in the topic under research, there is no framework that relates problems and solutions associated with collaborative processes in NHN. Andrés and Poler (2011) review some of the existing problems affecting the network collaborative processes and propose a collection of solutions that address the most relevant problems that affect collaborative processes caused by the inter- organizational barriers (Poler, Ortiz, Tormo, & Gutierrez, 2002). Furthermore, the degree of coverage analysis is proposed for identifying the extent to which the literature provided solutions, of the identified problems, can be adopted to address the problems in NHN context. In this way, if the solution is specifically provided for NHN it has an excellent degree of coverage for NHN ( ). Otherwise, if the solution is focused for HN it must be determined in which extent the provided solution can be implemented in NHN. For that, the solution is classified in a poor, unsatisfactory, acceptable or satisfactory degree of coverage (Andrés and Poler, 2011).
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Pediatric Collaborative Improvement Networks: Background and Overview

Pediatric Collaborative Improvement Networks: Background and Overview

a short-term collaborative. For example, all multisite improvement efforts need speci fi c infrastructure elements that require a signi fi cant initial investment of resources (eg, database devel- opment, recruitment of participating teams, and human subjects approvals). For a network, this investment is lever- aged as it provides a foundation for ongoing improvement and research involving the same sites and target pa- tient population. In contrast to a short- term collaborative, networks typically need a more robust information tech- nology (IT) system to deal with a large volume of data, from disparate sources, over a long period of time, to provide the capability to answer complex re- search questions. For these reasons, networks typically have a stronger level of sophistication in data management, analysis, and reporting, with resulting complex and resilient IT systems that can leverage this information for both improvement and research efforts. As part of its work to support networks, the Learning Networks Core of the Anderson Center for Health Systems Excellence at the Cincinnati Children ’ s Hospital Medical Center has grouped the systems and capabilities required for collaborative improvement net- works into 7 main areas (Fig 1) 34 :
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Collaborative research networks work

Collaborative research networks work

We strongly advocate the adoption of network structures, similar to that which we developed in Brazil, in other developing countries that are embrac- ing the challenge of sustained invest- ment in science and technology. We also urge that collaborative networks be more vigorously pursued in devel- oped countries, so that academic research might become more multidi- mensional and capable of tackling complex problems. We realize that this would depend on some fundamental changes in the manner in which indi- vidual scientists are evaluated and research funds dispersed. Nevertheless, we feel that we have amply demon- strated that research networks work, and that their implementation within the wealthy research communities of the US, Europe, and Japan could begin a whole new chapter in modern bio- medical research, enhancing the trans- lation of improved knowledge into concrete contributions to society.
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Roles for Children’s Hospitals in Pediatric Collaborative Improvement Networks

Roles for Children’s Hospitals in Pediatric Collaborative Improvement Networks

Children ’ s hospitals represent a signi fi cant opportunity to reduce morbidity, mortality, and costs, particularly for children with complex chronic conditions (CCCs) who comprise a disproportionate and growing share of admissions, readmissions, and resource use. Most children with CCCs are in some way associated with a children ’ s hospital, and the subspecialists who care for them are primarily concentrated in the ∼ 200 children ’ s hospitals in the United States. Children ’ s hospitals and their associated subspecialty clinics are uniquely positioned to achieve signi fi cant outcomes and cost savings through coordinated quality-improvement efforts. However, even the largest children ’ s hospital has relatively small volumes of patients with any given condition. Only by linking children ’ s hospitals in net- works can a suf fi cient “ N ” be achieved to build the evidence for what works for children. Large-scale pediatric collaborative network exem- plars have demonstrated the ability to improve outcomes, reduce costs, and spread changes found to be effective. Substantial oppor- tunities exist for networks to expand to additional conditions, im- provement topics, and sites, but fi nancial barriers exist. Although much of their participation has been funded as “ pay to participate ” efforts by the hospitals themselves, most fi nancial bene fi ts accrue to payers. As health care reform becomes a reality and fi nancial pres- sures intensify, it will become increasingly dif fi cult for children ’ s hospitals to serve as the primary source of support for networks. Partnerships between children ’ s hospitals and national payers to support collaborative networks are needed, and these partnerships have the potential to signi fi cantly improve pediatric care and out- comes, particularly for children with CCCs. Pediatrics 2013;131: S215 – S218
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Design and Management of Collaborative Intrusion Detection Networks

Design and Management of Collaborative Intrusion Detection Networks

Remark 6.2.1 The choice of using the word collaborative networks is to distinguish this approach from its cooperative counterpart. Cooperative networks often refer to a network of nodes that are able to act as a team and then split the team utility among the members. This will require global communications, coordination and bargaining. This appears to be unrealistic for IDN systems. In collaborative networks, nodes behave strategically not because they are selfish agents but because they are unable to coordinate or act as a team. Our work is essentially different from non-cooperative network formation problems, where all agents act selfishly to achieve their individual goals, which can be misaligned with each other. In our IDN design, the players have their goals aligned in a certain way to achieve efficient exchange of knowledge with each other. This is similar to classical strategic games such as Battle of the Sexes and Bach and Stravinsky game [98]. However, the goals become less aligned when agents have low trust values. This flexibility in the model essentially attributes to the reciprocal altruism.
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Physician Professionalism and Accountability: The Role of Collaborative Improvement Networks

Physician Professionalism and Accountability: The Role of Collaborative Improvement Networks

practices that are successfully imple- menting system changes such as those embodied in patient-centered medical homes or accountable care organiza- tions. These payment mechanisms, fo- cused as they are on broader population- based outcomes, could be used to sup- port and foster more participation of practices in collaborative networks. A collaborative improvement network almost by de fi nition ensures that quality is measured, that feedback and bench- marking occur, and that valid efforts are made to improve outcomes. As we have noted, providing feedback through per- formance reports is a necessary, but insuf fi cient, step to improve outcomes, because most physicians do not have the knowledge and skills to comprehensively improve their results. In a network, given more robust data, incentive payments could be based on how fast a practice improves or how much it improves, so the reward is for improvement, not just a comparative score. Networks represent an opportunity to encourage continual improvement in care and that aligns with payers ful fi lling their fi duciary re- sponsibility to purchase care that is of both high quality and high ef fi ciency. With the added impetus of aligned payment, collaborative improvement networks could augment the effectiveness of patient-centered medical homes or even an accountable care organization model by fostering improvement in the care system across care sites, at the local, regional, or, ideally, national level. Many insurer-purchasers seek to identify and purchase uniformly high-quality care across a large geographic or service area. Collaborative improvement net- works are designed to help ensure high- quality, reliable care across multiple sites, which may facilitate purchaser- payers in being able to offer a consis- tently higher quality of care and, when coupled with changes in reimbursement, could result in elimination of consider- able waste and inef fi ciency as well.
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Exemplar Pediatric Collaborative Improvement Networks: Achieving Results

Exemplar Pediatric Collaborative Improvement Networks: Achieving Results

A number of pediatric collaborative improvement networks have dem- onstrated improved care and outcomes for children. Regionally, Cin- cinnati Children ’ s Hospital Medical Center Physician Hospital Organization has sustained key asthma processes, substantially in- creased the percentage of their asthma population receiving “ perfect care, ” and implemented an innovative pay-for-performance program with a large commercial payor based on asthma performance mea- sures. The California Perinatal Quality Care Collaborative uses its out- comes database to improve care for infants in California NICUs. It has achieved reductions in central line – associated blood stream infec- tions (CLABSI), increased breast-milk feeding rates at hospital dis- charge, and is now working to improve delivery room management. Solutions for Patient Safety (SPS) has achieved signi fi cant improve- ments in adverse drug events and surgical site infections across all 8 Ohio children ’ s hospitals, with 7700 fewer children harmed and . $11.8 million in avoided costs. SPS is now expanding nationally, aim- ing to eliminate all events of serious harm at children ’ s hospitals. National collaborative networks include ImproveCareNow, which aims to improve care and outcomes for children with in fl ammatory bowel disease. Reliable adherence to Model Care Guidelines has produced improved remission rates without using new medications and a sig- ni fi cant increase in the proportion of Crohn disease patients not taking prednisone. Data-driven collaboratives of the Children ’ s Hospi- tal Association Quality Transformation Network initially focused on CLABSI in PICUs. By September 2011, they had prevented an estimated 2964 CLABSI, saving 355 lives and $103 722 423. Subsequent improve- ment efforts include CLABSI reductions in additional settings and populations. Pediatrics 2013;131:S196 – S203
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A Collaborative Access Control Framework for Online Social Networks

A Collaborative Access Control Framework for Online Social Networks

Controlling the content of the item is outside the scope of this article. Gay et al. [13] provided fine-grained privacy mechanism to control over sharing and re-sharing, and the distribution of re-shared messages in decen- tralized OSN. Similar to our work, they also base their enforcement of privacy policies on ReBAC. Despite this apparent similarity, our access control policy has more fine-grained features such as the possibility to define an explicit de- nied set and accessor specification policies. In their work, only trust is used to determine which users, among those who has already received the item, are allowed to propagate this item. In contrast, we apply trust as one of four components in the process of computing the collaborative decision regard- ing who can view or share a given item. That is, contrary to our approach, the proposal in [13] doses not consider the users associated with the item as co-controllers.
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Energy-optimal collaborative file distribution in wired networks

Energy-optimal collaborative file distribution in wired networks

In this paper, we narrow down the focus to energy consumption in file-sharing applications/systems. First, as demonstrated by previous studies [35], homes and organiza- tions (i.e., end-hosts) are responsible for 75 % of the overall Internet energy consumption, whereas networking devices (e.g., routers) and data centers are responsible for the other 25 %. Note that existing file distribution services, such as peer-to-peer (P2P) file sharing, one-click-hosting (OCH), software release, etc., represent a major fraction of current Internet traffic, ranging beween 18 and 30 percent [4, 19, 27]. File-sharing applications are run by PCs or laptops that are typically wired devices. The combined effect of the two previous arguments suggests that file-sharing applications are responsible for a significant portion of the overall energy consumption in the Internet. In addition, within the context of corporate/LAN networks, operations such as software updates can be defined also as file distribution processes. Most of previous studies in the area of optimizing file dis- tribution services have mainly focused on minimizing the download time [26, 28, 31, 33]. However, those algorithms designed to minimize the download time are not optimal in terms of energy consumption. Energy consumption in P2P systems is a well studied problem as indicated by numerous references in [32], but this study does not include the file distribution problem.
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Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

In this work we advocate studying this ubiquitous form of data and, by combining ideas from latent factor models for collaborative filtering and language modeling, propose a novel, flexible and expressive collaborative sequence model based on recurrent neural networks. The model is designed to capture a user’s contextual state as a personalized hidden vector by summarizing cues from a data-driven, thus variable, number of past time steps, and represents items by a real-valued embedding. We found that, by exploiting the inherent structure in the data, our formulation leads to an efficient and practical method. Furthermore, we demonstrate the versatility of our model by applying it to two different tasks: music recommendation and mobility prediction, and we show empirically that our model consistently outperforms static and non-collaborative methods.
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Capacity of Wireless Ad Hoc Networks with Opportunistic Collaborative Communications

Capacity of Wireless Ad Hoc Networks with Opportunistic Collaborative Communications

In this section, performance comparison between (central- ized) optimal MH and the (distributed) OST scheme pro- posed in [6] is presented in terms of achievable rates for ad hoc networks with no spatial reuse (i.e., multiple concurrent transmissions are not allowed). This requires to cast the opti- mal MH problem into a convenient framework (Section 3.1) and to exploit the results in [6] for the case where any num- ber of nodes can collaborate with the ongoing transmission (Section 3.2).

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Collaborative Filtering Approach For Big Data
          Applications in Social Networks

Collaborative Filtering Approach For Big Data Applications in Social Networks

Recommender systems [6] are techniques and intelligent Big data results in poor performance with respect to the applications to assist users in a decision making process computational part. So, there is a need to parallelize the where they want to and past purchases. Send tailored traditional data mining algorithms. There has been several recommendations to mobile devices while customers are research works carried out to handle and process the Big in the right area to take advantage of offers.Recalculate data. Google has developed a software framework called entire risk portfolios in minutes. Quickly identify Map Reduce to support large distributed data sets on customers who matter the most things are click stream clusters of computers, which is effective to analyse large analysis and data mining to detect fraudulent behaviour. amounts of data. Followed by Google’s work, many Collaborative filtering (CF) such as item- and user-based implementations of Map Reduce emerged and lots of methods are the dominant techniques applied in RSs. The traditional methods combined with Map Reduce have been basic assumption of user-based CF is that people who presented such as Apache Hadoop, Phoenix, Mars, and agree in the past tend to agree again in the future. Twister. Apache Hadoop is a software framework that Different with user-based CF, the item-based CF helps constructing the reliable, scalable distributed algorithm recommends a user the items that are similar to systems. Hadoop enables users to store
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COLLABORATIVE PLANNING OVER LOW BANDWIDTH TACTICAL NETWORKS

COLLABORATIVE PLANNING OVER LOW BANDWIDTH TACTICAL NETWORKS

Collaboration planning (CP) and the Inmarsat on the move satellite system were key enabling technologies for Corps and Division level commanders during Operation Iraqi Freedom (OIF). CP consists of planning tools such as whiteboarding, chat, voice conferencing, and file shar- ing with numerous collaborative tools commercially avail- able today. The DoD standard for collaboration is the Defense Collaboration Tool Suite (DCTS). While the In- marsat and DCTS capabilities provided significant im- provements in capability for the warfighter, the scalability of these technologies is limited. The commercial CP tools within DCTS such as Microsoft’s Net Meeting are opti- mized for operation on high speed wired networks, and consequently do not operate well over low bandwidth tac- tical networks. Additionally the Inmarsat architecture em- ployed is very cost prohibitive when scaled beyond a hand- ful of Army commanders. This paper provides an overview of a prototype Tactical Collaboration Tool (TCT) that was developed to operate over low bandwidth wireless net- works, along with recommendations for improving the In- marsat satellite architecture. The paper includes an analysis of the performance of the TCT over a simulated Inmarsat network, and contrasts it against commercial CP tools.
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Improving Domain-Specific Classification by Collaborative Learning with Adaptation Networks

Improving Domain-Specific Classification by Collaborative Learning with Adaptation Networks

Sufficient amount of labeled data is vital for machine learn- ing applications. However, it may not always be feasible to expend significant human effort for collecting and labeling data in many tasks. The objective of domain adaptation is to utilize the data in a label-rich (source) domain for in- ferring the class labels in a label-scarce (target) domain. In this domain adaptation setting, the source data may be sam- pled from a related but different distribution. How to effec- tively transfer knowledge learnt from source data is crucial for facilitating learning tasks in the target domain. A suc- cessful domain adaptation strategy is to learn cross-domain representations in a common space, such that the instances from different domains cannot be distinguished in the fea- ture space. Impressive progress has been achieved, espe- cially the adoption of deep convolutional neural networks in recent years. To guide network learning, some measures of distribution variance, e.g., Maximum Mean Discrepancy (MMD) (Long et al. 2015), are used, and the domain dis- Copyright c 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
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