Tuesday, 14 January 2020

Examining User Complaints of Wearable Apps:A Case Study on Android Wear

Examining User Complaints of Wearable Apps:A Case Study on Android Wear

 
Android Projects 2019 2020

Mobile apps are very popular and have been the focus ofnumerous studies in recent years [19], [24]. A fundamentalchange introduced by mobile apps is the way that they arereleased to users, which is through app stores. App storesallow users to directly provide feedback on the mobile appsthrough user reviews. Although these user reviews were meantto simply provide feedback about the apps, they proved to bemuch more useful. For example, studies have shown that theycan be used to understand user problems so that developerscan avoid low ratings, which can have a major impact on theapp’s user base and revenues [12], [14], [25].More recently, wearable devices have been introduced,which complement handheld devices. Wearable devices i.e.,smart watches and fitness trackers, are becoming increasinglypopular and are expected to reach 101 million devices by2020 [6]. Wearable devices provide developers with accessto unique sensors that can be used to enhance the user experi-ence [2]. As such, developers began to develop apps that arespecifically designed to run on these wearable devices, calledwearable apps. Wearable apps are different than handheldapps that run on mobile phones since they 1) often are verylightweight (resources wise), 2) meant to run on very smallscreens, 3) have access to a different set of sensors, and 4)heavily depend on the mobile device to perform the majority ofthe heavy processing. However, wearable devices have uniquecharacteristics that pose challenges when compared to otherplatforms or devices [26]. To the best of our knowledge, veryfew studies have focused on wearable apps to date.Therefore, similar to the prior studies on (handheld) mobileapp reviews [13], [15], [17], [28], we also investigate usercomplaints but our study focuses on complaints from users ofwearable apps. To perform our study, we manually classify589 reviews belonging to 6 wearable apps. The reviews weretagged by the first two authors of the paper and groupedinto 15 different categories. For each category, we measuredthe frequency of the complaints.

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Friday, 10 January 2020

Quality Workflow Management for different Android Platform Device


Quality Workflow Management for different Android Platform Device

Code Shoppy Android Projects

 

 Abstract-- Quality workflow management is a very important idea to achieve success in delivering good quality of the product to the customer in a competitive world by offering all the features that the customer needed in a minimal time and cost. Android platform devices are most widely used by all people. Intel atom is the processor that runs on all the smart devices. The framework is designed and implemented to achieve quality workflow management on different android platform using Intel atom. Learn More  Keywords---Quality Workflow management, Intel Atom, Android Platforms.

 INTRODUCTION 
Quality of the product is more attractive to people .The Quality of the product which we are delivering to the customer is very important. And it should be complaint free from the customer who are using the product. Hence we need to carefully manage the workflow of the system to achieve long run success of any organization. Android platform supported devices are evolving at a higher rate [1]. People prefer to use because of its quality, the service offering to the customer what is needed within minimal budget than compared to windows and iOS platforms. We can see the popular electronic device which is widely using with the popular version in the given Figure 1 Global Web Index [2] provides details about the most popular and commonly used electronic devices. As it can be seen, smartphone usage is almost equivalent to the PC/Laptop usage.  
Figure 2 gives information about usage trend of mobile devices [3] and desktop from the year 2007 to 2015. The curve depicting the number of mobile devices users each year has an upwards trajectory showing that mobile devices have almost replaced the desktop environment for most of the computing activities carried out by the users. Mobile devices are most widely used electronic devices since it provides all the computing activities as like high end devices. And it also provides the portable, lesscost user friendly environment for all users to prefer this device.Hence mobile devices work in an unaffected manner satisfying all the user requirements [4]. The race between manufacturers has increased the amount of complexity about fitting the set of constraints related to software into hardware [5]. Intel Atom processor is used to enable the services for both high end and low end devices such as Desktop devices, laptops and mobile devices. In which mobile devices are mostly popular and widely used.The block diagram of intel atom is as shown in the figure 3.
The speed of the Intel atom process varies and provides for heavy processor in a less time [6].Mobile devices are providing too many computing features when compared to old times, this in turn adds additional requirement demand on the chips which runs the mobile [devices 7]. Figure 3: Block diagram of Intel atom Figure 4 shows a block reference design of the smart phone containing Intel Atom and all the other components are the integrated in the smartphone [8]. Figure 4: Smartphone reference design includes all software and hardware components  
SYSTEM MODEL 
The workflow management has to be carried daily on the android device to check the Quality of the Intel atom. List of task that need to be perform is as given below: xAuthenticate the user to server with proper credentials. xObtain the latest build from the server xChecking the json file is present or not in the build xFlash the latest build on android device xUpdate the test environment in the system xIntegrate the test campaigns that need to be executed on android devices xObtain the logs and results from the device xUpload the result to reporting tool. xMailing the result to team. All these steps are carried out manually earlier, which consumed lot of human effort and exact time presence to process the work. If the person is not available for that particular moment to provide output of one as input to another, the process task will be delayed and can’t reach the target. Hence with the help of python scripting all these tasks are carried out independently by the system. 
This helps to achieve the accuracy in time and results than compared to manual system. And it also helps to achieve product quality and delivering the product in given time and cost so that the success of the organization can be achieved. The system architecture is as shown in figure 5.

Thursday, 2 January 2020

Design of Dynamic Vehicle Routing System

Design of Dynamic Vehicle Routing System

In traditional logistics distribution operation, vehicle routing plan is often made manually. However, when the number of customers and vehicles arise, transportation resources are usually utilized insufficiently. Therefore, an intelligent vehicle routing system will be very helpful to improve the distribution efficiency. Geographic information system (GIS) is a necessary part in a vehicle routing system. Two issues are essential for the development of a GIS system: map data sources and GIS developing platform. However, in China, the map data is too expensive, and also incompatible between different developing platforms. In addition, many GIS developing platforms like ArcGIS engine or MapX, are too specific and complex to use. Online map services can easily solve these problems: they share map data on the internet and provide developing APIs to public users for free. It's an alternative way to develop our own GIS application by using online map services. In real distribution process, customer orders emerge randomly.  

Code Shoppy


We need to plan our vehicle route dynamically to response these orders well. From this point of view, we need to know the real-time vehicle location and the real-time order status. Leveraged by the developing of GPS and wireless communication technology, we can now get real-time information easily. Based on the technologies mentioned above, this paper introduces a solution to develop dynamic vehicle routing system. After literature review, the principle of using online map service API is discussed and the detailed methods called in the vehicle routing system is reported. Then we explain the vehicle routing algorithm integrated in our system, which is called Variable Neighborhood Algorithm (VNS) algorithm. We also discuss the real-time requirements responding strategy and real-time traffic information handling strategy to draw real time distribution plan. After that, we report the design of this system, including data model design, system architecture design and system function design. 

A. Vehicle routing system The early studies on vehicle routing system choose the developing platforms offered by the professional GIS enterprises, such as ArcGIS, Mapinfo Professional, MapX and so on. HUO (2003) proposed a mathematics model of vehicle routing problem [1]. In this study, the authors use MapInfo Professional to partition the distribution region and then route each group individually. YANG (2010) combined GIS with logistics distribution technologies and developed a system by ArcGIS Engine [2].

B. Dynamic & Larger Scale Vehicle Routing Problem More and more attention has been paid to Dynamic Vehicle Routing Problem (DVRP) since the 1990s [3-4]. Different from static vehicle routing problem, dynamic vehicle routing problem considers uncertain information, including real-time service requests, time-dependent travel time between demand nodes and real-time vehicle control. In vehicle routing system, there is a clear need to develop solution procedures that are able to solve the large scale problems. https://codeshoppy.com/ For large scale problems, most VRP algorithms such as the well-known Clarke-Wright savings heuristics (Clark and Wright, 1964) [5] do not yield satisfying results within a reasonable amount of computation time. For the instance with 2400 uniformly distributed customers, for example, the Clarke­Wright savings heuristics would take several tens of hours to fmd the solution(Ouyang, 2007)[6]. Gehring et al. (1999) proposed standard benchmark problems of 1,000 customers. After that, much more research has been devoted to solve VRPs with up to 1,000 customers with various heuristics. These approaches to solve large-scale VRPTW can be categorized into two types: those that are developed to improve traditional heuristics algorithms by performing relatively broader search and avoiding the shortcoming of falling into local optima (KytOjoki, et aI., 2007[7]; Dondo and Cerda, 2009)[9]), and those that are based on the cluster-fIrst and routing-second principle (Ouyang, 2007)[6], which aims at reducing the problem size and computational time. Variable neighborhood search (VNS) was fIrst proposed by Mladenovic(1997) [10] and Hansen (1997)[11]. As a local search based metaheuristic, VNS behaves excellently in solving NP­hard problems. During the past few years, the VNS approach has been applied to variants of the VRPs effIciently. Kytojoki et al. (2007) presented an effIcient VNS heuristic, which is specifIcally aimed at solving very large scale real-life vehicle routing problems [7]. Polacek et ai. (2004) proposed a VNS heuristic to tackle the basic capacitated vehicle routing problem (CVRP) and achieved new best solutions in 71 cases [12].  Click Here