Based on a study of a postgraduate course, we show how-through the processes associated with applying a strategic tool-students developed the understandings that allowed them to span disciplinary and organizational boundaries. We reveal how the students, working in groups and acting as consultants to industry clients, developed specific boundary-spanning skills learned through observation and practice (mimesis), and reflection. Namely, (1) working with others with different disciplines to establish roles and processes to operate successfully as a group, (2) establishing productive communication with other groups of diverse disciplines as part of project processes, (3) eliciting information from other groups of diverse specialists, and (4) managing an inclusive discussion process among other groups of diverse specialists for agreement. We discuss how these insights about mimesis and reflection add to pedagogic debates about instruction for interdisciplinary and inter-organizational learning and the implications for management education and development practice.

This study tackles the online user allocation problem in mobile edge computing (MEC) systems powered by non-orthogonal multiple access. App vendors need to determine a proper wireless channel in a base station/edge server and sufficient transmit power for every user. We consider a stochastic MEC system where users arrive and depart over time. When an edge server runs out of computing resources, some users will have to wait until the resources become available again, which incurs an allocation delay cost. This cost is often not investigated in many studies, which also do not consider a multi-cell, multi-channel system as we do in this work, due to its complexity. We aim to minimize the allocation delay and transmit power costs, increasing the system's energy efficiency. To achieve this objective while guaranteeing users’ data rate requirements over time, we adopt the Lyapunov framework to convert this long-term optimization problem into a series of subproblems to be solved in every time slot. To solve the aforementioned subproblems efficiently, we present a distributed game theory-based approach. The proposed algorithm is theoretically evaluated and experimentally demonstrated to outperform several baseline and state-of-the-art methods, highlighting the significance of systematic consideration for both computation and communication aspects of this problem.

There is a large growth in interest in big data analytics to discover unknown patterns and insights. A major challenge in this domain is the need to combine domain knowledge - what the data means (semantics) and what it is used for - with data analytics and visualization techniques to mine and communicate important information from huge volumes of raw data. Many data analytics tools have been developed for both research and practice to assist in specifying, integrating and deploying data analytics and visualization applications. However, delivering such big data analytics application requires a capable team with different skillsets including data scientists, software engineers and domain experts. Such teams and skillset usually take a long time to build and have high running costs. An alternative is to provide domain experts and data scientists with tools they can use to do the exploration and analysis directly with less technical skills required. We present an overview and analysis of several current approaches to supporting the data analytics for end-users, identifying key strengths, weaknesses and opportunities for future research.

Mobile edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider's perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This article aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) – an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.

Mobile edge computing (MEC) allows edge servers to be placed at cellular base stations. App vendors like Uber and YouTube can rent computing resources and deploy latency-sensitive applications on edge servers for their users to access. Non-orthogonal multiple access (NOMA) is an emerging technique that facilitates the massive connectivity of 5G networks, further enhancing the capability of MEC. The edge user allocation (EUA) problem faces new challenges in 5G NOMA-based MEC systems. In this study, we investigate the EUA problem in a multi-cell multi-channel downlink power-domain NOMA-based MEC system. The main objective is to help mobile app vendors maximize their benefit by allocating maximum users to edge servers in a specific area at the lowest computing resource and transmit power costs. To this end, we introduce a decentralized game-theoretic approach to effectively select a channel and edge server for each user while fulfilling their resource and data rate requirements. We theoretically and experimentally evaluate our solution, which significantly outperforms various state-of-the-art and baseline approaches.

Edge computing is a new distributed computing paradigm extending the cloud computing paradigm, offering much lower end-to-end latency, as real-time, latency-sensitive applications can now be deployed on edge servers that are much closer to end-users than distant cloud servers. In edge computing, edge user allocation (EUA) is a critical problem for any app vendors, who need to determine which edge servers will serve which users. This is to satisfy application-specific optimization objectives, e.g., maximizing users’ overall quality of experience, minimizing system costs, and so on. In this article, we focus on the cost-effectiveness of user allocation solutions with two optimization objectives. The primary one is to maximize the number of users allocated to edge servers. The secondary one is to minimize the number of required edge servers, which subsequently reduces the operating costs for app vendors. We first model this problem as a bin packing problem and introduce an approach for finding optimal solutions. However, finding optimal solutions to the $\mathcal {NP}$ -hard EUA problem in large-scale scenarios is intractable. Thus, we propose a heuristic to efficiently find sub-optimal solutions to large-scale EUA problems. Extensive experiments conducted on real-world data demonstrate that our heuristic can solve the EUA problem effectively and efficiently, outperforming the state-of-the-art and baseline approaches.

We describe the use of a suite of human-centric, domain-specific visual languages to manage knowledge for data-intensive systems. We use two exemplar system case studies – a smart home to support ageing people and a set of smart city technologies – to motivate the need for such an approach. We then describe aspects of these two example systems from abstract requirements to specific data analysis, implementation and deployment choices using our BiDaML representation. We discuss the strengths and limitations of the approach and key directions for further work in this area.

We present BiDaML (Big Data Analytics Modeling Languages), an integrated suite of visual languages and supporting tool to help end-users with the engineering of big data analytics solutions.BiDaML, our visual notations suite, comprises six diagrammatic notations: brainstorming diagram, process diagram, technique diagrams, data diagrams, output diagrams and deployment diagram.BiDaML tool provides a platform for efficiently producing BiDaML visual models and facilitating their design, creation, code generation and integration with other tools.To demonstrate the utility of BiDaML, we illustrate our approach with a realworld example of traffic data analysis.We evaluate BiDaML using two types of evaluations, the physics of notations and a cognitive walkthrough with several target end-users e.g.data scientists and software engineers.

Big data and analytics are increasingly used in different domains to gain insights and to improve decision-making. Developing big data analytics solutions is a complex task involving multidisciplinary teams and users - with no data science and programming background - to professional data scientists and software engineers. Different stakeholders work with a variety of data types, tasks and concepts in different languages from high- level domain concepts to low level programming languages and technical concepts. In order to advance the level of abstraction beyond low-level data analysis technical details, we demonstrate our BiDaML tool. BiDaML brings all stakeholders around one tool to specify, model and document their big data applications using a novel set of domain-specific visual languages (DSVLs).

As online services and applications are moving towards a more human-centered design, many app vendors are taking the quality of experience (QoE) increasingly seriously. End-to-end latency is a key factor that determines the QoE experienced by users, especially for latency-sensitive applications such as online gaming, autonomous vehicles, critical warning systems and so on. Edge computing has then been introduced as an effort to reduce network latency. In a mobile edge computing system, edge servers are usually deployed at, or near cellular base stations, offering processing power and low network latency to users within their proximity. In this work, we tackle the edge user allocation (EUA) problem from the perspective of an app vendor, who needs to decide which edge servers to serve which users in a specific area. Also, the vendor must consider the various levels of quality of service (QoS) for its users. Each QoS level leads to a different QoE level. Thus, the app vendor also needs to decide the QoS level for each user so that the overall user experience is maximized. We first optimally solve this problem using Integer Linear Programming technique. Being an NP-hard problem, it is intractable to solve it optimally in large-scale scenarios. Thus, we propose a heuristic approach that is able to effectively and efficiently find sub-optimal solutions to the QoE-aware EUA problem. We conduct a series of experiments on a real-world dataset to evaluate the performance of our approach against several state-of-the-art and baseline approaches.

We present BiDaML 2.0, an integrated suite of visual languages and supporting tool to help multidisciplinary teams with the design of big data analytics solutions. BiDaML tool support provides a platform for efficiently producing BiDaML diagrams and facilitating their design, creation, report and code generation. We evaluated BiDaML using two types of evaluations, a theoretical analysis using the “physics of notations”, and an empirical study with 1) a group of 12 target end-users and 2) five individual end-users. Participants mostly agreed that BiDaML was straightforward to understand/learn, and prefer BiDaML for supporting complex data analytics solution modeling than other modeling languages.

In edge computing, edge servers are placed in close proximity to end-users. App vendors can deploy their services on edge servers to reduce network latency experienced by their app users. The edge user allocation (EUA) problem challenges service providers with the objective to maximize the number of allocated app users with hired computing resources on edge servers while ensuring their fixed quality of service (QoS), e.g., the amount of computing resources allocated to an app user. In this paper, we take a step forward to consider dynamic QoS levels for app users, which generalizes but further complicates the EUA problem, turning it into a dynamic QoS EUA problem. This enables flexible levels of quality of experience (QoE) for app users. We propose an optimal approach for finding a solution that maximizes app users' overall QoE. We also propose a heuristic approach for quickly finding sub-optimal solutions to large-scale instances of the dynamic QoS EUA problem. Experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approaches against a baseline approach and the state of the art.

In mobile edge computing, edge servers are geographically distributed around base stations placed near end-users to provide highly accessible and efficient computing capacities and services. In the mobile edge computing environment, a service provider can deploy its service on hired edge servers to reduce end-to-end service delays experienced by its end-users allocated to those edge servers. An optimal deployment must maximize the number of allocated end-users and minimize the number of hired edge servers while ensuring the required quality of service for end-users. In this paper, we model the edge user allocation (EUA) problem as a bin packing problem, and introduce a novel, optimal approach to solving the EUA problem based on the Lexicographic Goal Programming technique. We have conducted three series of experiments to evaluate the proposed approach against two representative baseline approaches. Experimental results show that our approach significantly outperforms the other two approaches.

It is well recognized that traceability links between software artifacts provide crucial support in comprehension, efficient development, and effective management of a software system. However, automated traceability systems to date have been faced with two major open research challenges: how to extract traceability links with both high precision and high recall, and how to efficiently visualize links for complex systems because of scalability and visual clutter issues. To overcome the two challenges, we designed and developed a traceability system, DCTracVis. This system employs an approach that combines three supporting techniques, regular expressions, key phrases, and clustering, with information retrieval (IR) models to improve the performance of automated traceability recovery between documents and source code. This combination approach takes advantage of the strengths of the three techniques to ameliorate limitations of IR models. Our experimental results show that our approach improves the performance of IR models, increases the precision of retrieved links, and recovers more correct links than IR alone. After having retrieved high-quality traceability links, DCTracVis then utilizes a new approach that combines treemap and hierarchical tree techniques to reduce visual clutter and to allow the visualization of the global structure of traces and a detailed overview of each trace, while still being highly scalable and interactive. Usability evaluation results show that our approach can effectively and efficiently help software developers comprehend, browse, and maintain large numbers of links.

Whilst robots are increasingly being deployed as social agents, it is still difficult to program them to interact socially. To create usable tools for programming these robots, tool developers need to know what abstraction levels are appropriate for programming social robot applications. We explore this through the iterative design and evaluation of an API for programming social robots. The results show that high level primitives, with a close mapping to social interaction, are suitable for programming social robot applications. However, the abstraction level should not be so high that it takes away too much control from programmers. This has the potential to enable programmers to produce high quality social robot applications with less programming effort.

Requirements captured by requirements engineers (REs) are commonly inconsistent with their client's intended requirements and are often error prone. There is limited tool support providing end-to-end support between the REs and their client for the validation and improvement of these requirements. We have developed an automated tool called MaramaAIC (Automated Inconsistency Checker) to address these problems. MaramaAIC provides automated requirements traceability and visual support to identify and highlight inconsistency, incorrectness and incompleteness in captured requirements. MaramaAIC provides an end-to-end rapid prototyping approach together with a patterns library that helps to capture requirements and check the consistency of requirements that have been expressed in textual natural language requirements and then extracted to semi-formal abstract interactions, essential use cases (EUCs) and user interface prototype models. It helps engineers to validate the correctness and completeness of the EUCs modelled requirements by comparing them to "best-practice" templates and generates an abstract prototype in the form of essential user interface prototype models and concrete User Interface views in the form of HTML. We describe its design and implementation together with results of evaluating our tool's efficacy and performance, and user perception of the tool's usability and its strengths and weaknesses via a substantial usability study. We also present a qualitative study on the effectiveness of the tool's end-to-end rapid prototyping approach in improving dialogue between the RE and the client as well as improving the quality of the requirements.

Most performance engineering approaches focus on understanding the use of runtime resources. However such approaches do not quantify the value being provided in return for the consumption of these resources. Without such a measure it is not possible to compare the efficiency of these components (that is whether the runtime cost is reasonable given the benefit being provided). We have created an empirical approach that measures the value being provided by a code path in terms of the visible data it generates for the rest of the application. Combining this with traditional performance cost data, creates an efficiency measure for every code path in the application. We have evaluated our approach using the DaCapo benchmark suite, demonstrating our analysis allows us to quantify the efficiency of the code in each benchmark and find real optimisation opportunities, providing improvements of up to 36% in our case studies.

One of the key problems with Software Architecture Documents (ADs)(2) is the difficulty of finding information required from them. Most existing studies focus on the production of ADs or Architectural Knowledge (AK)(3), to allow them to support information finding. However, there has been little focus placed on the consumption of ADs. To address this, we postulate the existence of a concept of "usage based chunks" of architectural information discoverable from consumers' usage of ADs when they engage in information-seeking tasks. In a set of user studies, we have found evidence that such usage-based chunks exist and that useful chunks can be identified from one type of usage data, namely, consumer's ratings of sections of ADs. This has implications for tool design to support the effective reuse of AK. (C) 2016 Elsevier Inc. All rights reserved.

The majority of existing application profiling techniques aggregate and report performance costs by method or calling context. Modern large-scale object-oriented applications consist of thousands of methods with complex calling patterns. Consequently, when profiled, their performance costs tend to be thinly distributed across many thousands of locations with few easily identifiable optimisation opportunities. However experienced performance engineers know that there are repeated patterns of method calls in the execution of an application that are induced by the libraries, design patterns and coding idioms used in the software. Automatically identifying and aggregating costs over these patterns of method calls allows us to identify opportunities to improve performance based on optimising these patterns. We have developed an analysis technique that is able to identify the entry point methods, which we call subsuming methods, of such patterns. Our offline analysis runs over previously collected runtime performance data structured in a calling context tree, such as produced by a large number of existing commercial and open source profilers. We have evaluated our approach on the DaCapo benchmark suite, showing that our analysis significantly reduces the size and complexity of the runtime performance data set, facilitating its comprehension and interpretation. We also demonstrate, with a collection of case studies, that our analysis identifies new optimisation opportunities that can lead to significant performance improvements (from 20% to over 50% improvement in our case studies).

Real-time computation of data streams over affordable virtualized infrastructure resources is an important form of data in motion processing architecture. However, processing such data streams while ensuring strict guarantees on quality of services is problematic due to: (i) uncertain stream arrival pattern; (ii) need of processing different types of continuous queries; and (iii) variable resource consumption behavior of continuous queries. Recent work has explored the use of statistical techniques for resource estimation of SQL queries and OLTP workloads. All these techniques approximate resource usage for each query as a single point value. However, in data stream processing workloads in which data flows through the graph of operators endlessly and poses performance and resource demand fluctuations, the single point resource estimation is inadequate. Because it is neither expressive enough nor does it capture the multi-modal nature of the target data. To this end, we present a novel technique which uses mixture density networks, a combined structure of neural networks and mixture models, to estimate the whole spectrum of resource usage as probability density functions. The proposed approach is a flexible and convenient means of modeling unknown distribution models. We have validated the models using both the linear road benchmark and the TPC-H, observing high accuracy under a number of error metrics: mean-square error, continuous ranked probability score, and negative log predictive density.