Service Aggregation Research Articles

ОСОБЕННОСТИ НАЛОГОВОГО УЧЕТА И ИСЧИСЛЕНИЯ НДФЛ ПРИ НАЛОГООБЛОЖЕНИИ ДОХОДОВ ВОДИТЕЛЕЙ ЯНДЕКС.ТАКСИ

The article examines the features of tax accounting and the calculation of personal income tax associated with the problem of taxation of the income of drivers of the Yandex.Taxi aggregator service, a step-by-step algorithm for the operation of the online taxi ordering system, identifies problems associated with the responsibility of the participants in the process, as well as with the occurrence of situations when the income of drivers is not taxed. As a result, it is concluded that it is necessary to oblige Yandex.Taxi LLC at the legislative level to conclude an employment contract with drivers and act as a tax agent in respect of the income they receive in order to increase tax control over the completeness of the calculation and payment of personal income tax, as well as to clearly define the boundaries of liability of the participants in the process online taxi ordering.

Contributions to ventilation system demand response: a case study of an educational building

The increasing share of volatile renewable energy in the electricity grid increases the importance of load flexibility and Demand Response for balancing electricity supply with demand. Flexible loads in office buildings (e.g. educational buildings) are heating, ventilation, and air conditioning (HVAC) systems. This paper focuses on ventilation systems as flexible loads for providing ancillary services to the grid. A number of studies consider ventilation system control based only on demand or discuss possibilities of improving system performance. Previous studies provide little or no information about ventilation system flexibility, e.g. amount of power modulation, the rate of change, and the duration of how long the power level can be held. The described information is required by aggregators to provide load aggregation services for transmission system operators (TSO). This paper proposes a robust and model-free approach to estimate ventilation system flexibility according to CO2 concentration in extracted air. The proposed approach includes power regulation boundaries for the ventilation system and duration estimation when operating at the selected boundary. A case study is conducted on a ventilation system, which services an auditorium of an educational building. The current paper analyzes the proposed robust approach for estimating ventilation system flexibility and compares estimation to measured results.

Discrete Choice Model Estimation with Revealed Preference Data and Aggregate Service Level Variables

Discrete Choice Model Estimation with Revealed Preference Data and Aggregate Service Level Variables

Aggregate Cyber-Risk Management in the IoT Age: Cautionary Statistics for (Re)Insurers and Likes

In this paper, we provide (i) a rigorous general theory to elicit conditions on (tail-dependent) heavy-tailed cyber-risk distributions under which a risk management firm might find it (non)sustainable to provide aggregate cyber-risk coverage services for smart societies, and (ii)a real-data driven numerical study to validate claims made in theory assuming boundedly rational cyber-risk managers, alongside providing ideas to boost markets that aggregate dependent cyber-risks with heavy-tails.To the best of our knowledge, this is the only complete general theory till date on the feasibility of aggregate cyber-risk management.

Services Trade Policies and Economic Integration: New Evidence for Developing Countries

AbstractThis paper applies machine learning to recreate to a high degree of accuracy the OECD's Services Trade Restrictiveness Index (STRI) to provide quantitative evidence on the restrictiveness of services policies in 2016 for a sample of developing countries, using regulatory data collected by the World Bank and WTO. Resulting estimates are used to extend the OECD STRI approach to 23 additional countries, producing what we term a Services Policy Index (SPI). Converting the SPI to ad valorem equivalent terms shows that services policies are typically much more restrictive than tariffs on imports of goods, in particular in professional services and telecommunications. The SPI has strong explanatory power for bilateral trade in services at the sectoral level, as well as for aggregate goods and services trade.

Rule-based Data Quality Intelligent Monitoring System

The rapid development of Internet information technology brought great convenience to people’s lives and work, but it also brought the problem of information overload[l]. The data center is the middle layer that mediates the contradiction between the foreground and the background[2]. It provides a reusable, standardized, and agile multifunctional platform through the modeling of background data and the aggregation of data services to support the foreground for the demand[3] of quickly change according to market changes. Through the data center, we have opened up the data islands of each business system. However, when each business system uses data, it found that the data quality is poor, unstable, not timely, and inconsistent. Traditional cluster data quality monitoring has the characteristics of inconsistent data quality requirements for various services, difficult development, low processing efficiency, difficult maintenance, and insufficient intelligence.Based on this situation, we design a data quality monitoring system based on rules and artificial intelligence. General users perform simple configuration on the interface to complete real-time online monitoring of data, and real-time statistics on the number of data quality problems and a detailed list for users to trace the cause of the problem and identify abnormal scenarios in advance. The comparison between experiments and mainstream products proves that the system has greatly improved in terms of user use and flexibility.

The value of aggregate service levels in stochastic lot sizing problems

Dealing with demand uncertainty in multi-item lot sizing problems poses huge challenges due to the inherent complexity. The resulting stochastic formulations typically determine production plans which minimize the expected total operating cost while ensuring that a predefined service level constraint for each product is satisfied. We extend these stochastic formulations to a more general setting where, in addition to the individual service level constraints, an aggregate service level constraint is also imposed. Such a situation is relevant in practical applications where the service level aggregated from a variety of products must be collectively satisfied. These extended formulations allow the decision maker to flexibly assign different individual service levels to different products while ensuring that the overall aggregate service level is satisfied and these aggregate service level measures can be used in conjunction with the commonly adopted individual service levels. Different mathematical formulations are proposed for this problem with different types of service levels. These formulations are a piece-wise linear approximation for the β, γ, and δ service levels and a quantile-based formulation for the αc service level. We also present a receding horizon implementation of the proposed formulations which can be effectively used in a dynamic environment. Computational experiments are conducted to analyze the impact of aggregate service levels and demonstrate the value of the proposed formulations as opposed to standard service levels imposed on individual items.

CDA: Coordinating data dissemination and aggregation in heterogeneous IoT networks using CTC

This paper presents a CDA mechanism to simultaneously coordinate data dissemination and data aggregation services in coexisting heterogeneous IoT networks (i.e., WiFi and ZigBee) using a WiFi Access Point (AP). With CDA, the WiFi signal interference to the low power ZigBee network is mitigated via the latest Cross-Technology Communication (CTC) techniques. The CTC techniques used in CDA are compatible with existing commodity IoT devices, which requires no hardware modification when implementing CDA. Specifically, the individual network status (e.g., duty cycle information) is first exchanged between different networks via CTC. Accordingly, the AP dynamically schedules its channel occupation time based on the communication demand and network status of both networks. Furthermore, a theoretical guidance is provided for the duty cycle control of the ZigBee network in coexisting scenarios, which guarantees the aggregation performance and extends the network's life-time. The proposed CDA mechanism is evaluated through a real-world testbed consisting of 2 WiFi devices and 40 ZigBee nodes. Experimental results show that CDA not only optimizes the ZigBee network's delay and life-time by 3.72 × and 1.24 × , respectively, but also improves the WiFi throughput by 1.94 × .

Survey and assessment of technical and economic features for the provision of frequency control services by household-prosumers

This paper surveys and assesses the technical and economic features that are crucial in the provision of frequency control services from aggregated household-prosumers. A framework is presented that allows policy makers to accurately assess these services. Furthermore, the role of the service aggregator in the provision from prosumers is outlined, something that highlights the value of the new distributed framework. Also discussed are the key features within the overall context of the design variables in various European countries, which are representative of different synchronous areas. This finding implies that from a regulatory point of view, important efforts are being made to harmonise the different network codes in the investigated countries. Moreover, this research compares the impact of techno-economic design variables on the financial incomes of providing frequency control services. For this purpose, we used a stochastic model of input variables that involves the statistical analysis of time series. A probabilistic method based on Monte Carlo simulation allowed the assessment of the revenues. The outcomes indicate that the RES penetration in each country together with the ratios of the power availability band and of the power availability band activation, and their associated prices are crucial factors that influence the resulting profitability.

Analysis of ride-sharing with service time and detour guarantees

This paper explores whether upper bound guarantees to detour distances can be introduced in ride sharing services. By ride sharing we mean taxi ride aggregation services such as Uber-Pool. The paper develops an analytical model that for a given demand relates the guarantee levels to (i) the percent of rides that can be matched, (ii) the expected vehicle distance traveled; (iii) the expected passenger distance traveled; (iv) the fleet size required, and (v) the average passenger trip time including waiting and riding. The formulas developed reveal that for the full range of feasible fleet sizes, ridesharing with detour distance guarantees outperforms both ordinary ride-sharing and ordinary taxi. This suggests that there is a business opportunity that is not currently being exploited.

Electric Vehicle Driver Response Evaluation in Multiaggregator Charging Management With EV Routing

Electric vehicle (EV) charging scheduling is a necessary aspect for encouraging the everyday use of EVs. EV charging may be accommodated while benefiting one or more of the stakeholders, i.e., the grid, the aggregator (service provider), and/or the driver. This article presents a profit maximization multiaggregator scheduling scheme, which encapsulates the benefits of the consumer while maximizing the profits of the aggregator in noncollaborative and collaborative scheduling. The manuscript proposes novel fuzzy inference based EV driver response indicator (EVDRI) for gauging the response of the EV driver to a particular scheduling. The EVDRI is dependent on parameters of charging time and cost. Penalties for low satisfaction, along with penalties for any EVs that remain unscheduled are also included in the profit calculation. Furthermore, the potential impact of EV routing on aggregator profits and driver's satisfaction is also explored. Results indicate that the user response is positive (greater satisfaction) in case of collaborative scheduling and with optimum routing. The developed fuzzy EVDRI (F-EVDRI) provides good indication of the satisfaction level of the drivers. Impact of different parameters is also explored on the F-EVDRI wherein charging duration was found to be the parameter that impacts the EVDRI.

The power of two-sided platforms to disseminate resistant innovations

PurposePlatforms have often been considered an excellent example of innovation with an incredibly fast diffusion rate due to network effects. Notwithstanding, in many cases, they match the definition of resistant innovations, requiring a significant change in the consumers' behavior that may slow down the process of diffusion. How can network effects be leveraged to disseminate resistant innovation based on a platform structure?Design/methodology/approachThis research focuses on the car-sharing industry as a single case study, involving five different providers and two service aggregators operating in Milan, Italy.FindingsFirst, this research shows how direct network externalities on the providers' side may play a vital role in the dissemination process, increasing the value perceived by the potential new entrants. Second, it shows how co-opetition dynamics, eventually encouraging multihoming phenomenon, may play – in the first phases of the diffusion process – a pivotal role to let the industry flourish.Research limitations/implicationsThis research contributes to the growing literature on platforms and two-sided platform showing how this model may be applied more broadly to network businesses to understand competitive dynamics.Practical implicationsThis study offers insights to managers and practitioners dealing with network services, showing the potential benefits of coopetitive strategies while facing the initial phases of the dissemination process.Originality/valueThis paper gets together resistant innovation, two-sided platforms and network effects offering novel insights on the dynamics of network services – such as the car-sharing – while giving fresh insights taking the perspective of the service providers.

Privacy‐aware cloud ecosystems: Architecture and performance

SummaryWith an increasing number of cloud providers offering services made use of by both individual users and other providers, there is a realization that service provision now involves an “ecosystem” of providers. Some providers may be directly visible to a user, while others may be contributors to composite services and not directly known to the user—as only the provider offering the composite service is visible. Such services may include: domain specific services (eg, simulation), advertising services, or profiling/analytics services. Understanding the impact on data privacy of a user for such a composite service remains a challenge, and providing transparency (and obtaining user consent for data use) remains a key requirement of the European General Data Protection Regulation (GDPR). An architecture that makes use of blockchains and smart contracts is proposed that addresses this requirement. An implementation of the architecture is used to demonstrate how access control can be managed and audited. The scalability and cost of undertaking access control, as the number of actors (both service providers and “voters”) increases, is also described. The proposed approach can be used to support service aggregation across both private and public clouds.

Aid for Trade and Trade in Services

Existing research generally finds weak positive effects of aid for trade (AfT) on aggregate merchandise trade of recipients once endogeneity in the AfT-trade relationship is accounted for. In this paper, we confirm weak findings for both aggregate merchandise and services trade of recipients, using GMM and IV estimations. Moreover, estimates lose statistical significance if non-AfT explanatory variables are treated as endogenous in estimation suggesting identification issues may not have been adequately addressed in extant work. We then examine an alternative proposition: that effects of AfT and different categories of AfT may be observed along the conditional distributions of exports and imports. Our findings confirm this hypothesis. AfT allocated to economic infrastructure, productive capacity building in services and trade policies and regulation is more effective for smaller trading economies, especially in services. We also observe considerable heterogeneity in the trade effects of AfT allocated to individual services sectors, indicating the importance of country-specific diagnostics in targeting AfT allocation.

What is the impact of psychiatric decision units on mental health crisis care pathways? Protocol for an interrupted time series analysis with a synthetic control study

BackgroundThe UK mental health system is stretched to breaking point. Individuals presenting with mental health problems wait longer at the ED than those presenting with physical concerns and finding a bed when needed is difficult – 91% of psychiatric wards are operating at above the recommended occupancy rate. To address the pressure, a new type of facility – psychiatric decision units (also known as mental health decision units) – have been introduced in some areas. These are short-stay facilities, available upon referral, targeted to help individuals who may be able to avoid an inpatient admission or lengthy ED visit. To advance knowledge about the effectiveness of this service for this purpose, we will examine the effect of the service on the mental health crisis care pathway over a 4-year time period; the 2 years proceeding and following the introduction of the service. We use aggregate service level data of key indicators of the performance of this pathway.MethodsData from four mental health Trusts in England will be analysed using an interrupted time series (ITS) design with the primary outcomes of the rate of (i) ED psychiatric presentations and (ii) voluntary admissions to mental health wards. This will be supplemented with a synthetic control study with the same primary outcomes, in which a comparable control group is generated for each outcome using a donor pool of suitable National Health Service Trusts in England. The methods are well suited to an evaluation of an intervention at a service delivery level targeting population-level health outcome and the randomisation or ‘trialability’ of the intervention is limited. The synthetic control study controls for national trends over time, increasing our confidence in the results. The study has been designed and will be carried out with the involvement of service users and carers.DiscussionThis will be the first formal evaluation of psychiatric decision units in England. The analysis will provide estimates of the effect of the decision units on a number of important service use indicators, providing much-needed information for those designing service pathways.Trial registrationprimary registry: isrctn.com Identifying number: ISRCTN77588384 Link: Date of registration in primary registry: 27/02/2020.Primary sponsorSt George’s, University of London, Cramner Road, Tooting, SW17 ORE. Primary contact: Joe Montebello.

Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid

Vehicle-to-grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique’s ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle-to-grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to predict total vehicle availability and found that automated machine learning achieved the best performance with an accuracy of 91.4%. Such technology would be of value to vehicle-to-grid aggregation services.

The Operation Mode of Distributed Energy System under the Background of Energy Interconnection

This paper analyses the typical operation mode of distributed energy system under the background of energy interconnection and proposes the “monopoly of purchase and marketing” operation mode and the “aggregation service provider” operation mode of energy interconnection. Between them, the latter covers two main auxiliary services including frequency regulation service and reserve service. Then, the method for calculating the expected cost and benefit of the illustrated model is proposed in this paper. Taking the Pearl Industrial Park of Guangdong Province as an example, the numerical analysis is carried out to obtain the cost and benefit of the distributed energy system under the above two modes. Finally, we use the control variable method to analyse the impact of key parameters on the static investment payback period, including grid price, PV subsidy price, auxiliary service price, output distribution coefficient, etc.

An effective service-oriented networking management architecture for 5G-enabled internet of things

The development of a 5G-enabled Internet of Things has led to a dramatic increase in network traffic load, which has presented tremendous challenges to network management. In this paper, a service-oriented network architecture is proposed to support the effective management of 5G-enabled IoT systems. This architecture effectively reduces the traffic load and simplifies network management by introducing a service aggregation and caching (SAaC) scheme. Specifically, SAaC first breaks through the data-centric network architecture by converting data into services. Then, SAaC significantly reduces traffic load and energy consumption by service aggregation. Finally, SAaC introduces service caching, and each content router caches new services locally after aggregating received services so that user requests are handled at the network layer. Experimental results demonstrate that compared with traditional solutions, the SAaC scheme improves the request response time by 20.52%–56.09%, reduces the traffic load by 10.85%–37.67%, and reduces energy consumption by more than 50%.

Modeling and Evaluation of Service Composition in Commercial Multiclouds Using Timed Colored Petri Nets

The increasing demand for Web services encourages commercial cloud service providers to publish their own services with various functional and nonfunctional capabilities in different cloud platforms. The aggregation of atomic services from multiple service repositories is the main idea of the service composition concept in multiclouds. The cloud Web service composition is a suitable way for satisfying users’ complex requests by integrating services from different clouds in order to create a new value-added composite service. The time required to serve a composite service by a multicloud environment is an important parameter, which depends on different factors, ranging from the service composition and selection algorithm to the number of atomic services published in the clouds. In this paper, a model based on timed colored Petri nets (TCPNs) is proposed to evaluate the service composition in multicloud environments while minimizing the number of clouds involved in serving a composite service request. The proposed TCPN graphically models the process of request submission, composite service analysis, service selection, and service provisioning in a multicloud environment. It also assesses both mean response time of the environment and probability of dropping composite requests. The verification of the accuracy of the proposed model is done by comparing the results obtained from the TCPN model, in two different scenarios, with the results from the CloudSim framework. These results confirm that our proposed TCPN model can appropriately model the system and evaluate its performance more efficiently than the CloudSim.

Learning Club, Home Court, and Magnetic Field: Facilitating business model portfolio extension with a multi-faceted corporate ecosystem

While scholarly interest in corporate level business model (BM) portfolios is increasing, little is known on the BM portfolio extension process. Positing that a corporate ecosystem can serve as an important context of this process, this paper addresses this gap by analysing an e-commerce giant's BM-launch-events that plot its BM portfolio extension journey. We find that the ecosystem, which comprises actors who hold resources and interact to coevolve, could be leveraged to facilitate a BM launch in manners beyond relying on activity-sharing synergies across BMs. Specifically, three configurations, each of which consists of a BM launch challenge, an ecosystem facet (i.e., Learning Club, Home Court, or Magnetic Field), and a leveraging strategy (i.e., Knowledge Orchestrator, Capability Bundler, or Service Aggregator), are identified to depict the associated process. Our findings offer a dynamic understanding of BM portfolio extension by integrating the actor-orientation of ecosystem and the activity-based view of BM. We also contribute to ecosystem literature by shedding light on a corporate ecosystem, revealing its generative properties, and linking its role to corporate strategy literature.