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Communications of the ACM

121 - 130 of 1,215 for bentley

Rivet Counting and Ocean Crossing: Case Examples Illuminating the Fracticality of the Theory-Practice Cycle and the Importance of Horizon Expansion

While the "UX" field redefines itself -- generalist versus specialist, academic versus practitioner -- we often fail to see the importance of the cyclical nature of our endeavors as contributors to an evolving body of both theoretical and practical knowledge. A cycle wholly necessary for progress that involves courage situationally and precision longitudinally. We illustrate this assertion with a historical example: the remarkable "Grand Tour" of the HMS Challenger in 1872, as well as case examples drawn from our work illustrating where theory and practice threatened to collide and swamp us, where the practicalities of established approaches and unexpected hurtles threw us into the fractal maelstrom that occurs when the known meets the unknown. We share these experiences and lessons learned in the spirit of fracticality (a term we will define). These "remixes" illustrate the importance of the theory-practice cycle for progress, and offer tips for breaking the rules.

Exploring the Landscape of Data Science

The panel will discuss and answer questions the landscape of employment and education pathways in data science and analytics. The panel will also talk about the current discussions within ACM and the role(s) the information technology discipline should have in the field. Finally the panel will solicit feedback from the audience on current work and desired next steps in order to address the global education and workforce needs.

Understanding Secondary Content Practices for Television Viewing

Secondary content experiences related to television viewing have been a frequent topic of study in the TVX community. While many new interfaces have been created and studied in the small scale, we are not aware of any larger quantitative work to study current practices now that many secondary content experiences are publicly available. We conducted a survey with a broad sample of the American population to explore current secondary content use. We report on our findings, including that 80% of these experiences occur before or after viewing the primary content, and not as simultaneous experiences, and that social posting about television content remains quite low, even for one's favorite show. We conclude with implications for the design new secondary content systems based on our findings.

DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN

At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation” that won the conference’s best paper award. In this technical correspondence, we want to point out some inaccuracies in the way DBSCAN was represented, and why the criticism should have been directed at the assumption about the performance of spatial index structures such as R-trees and not at an algorithm that can use such indexes. We will also discuss the relationship of DBSCAN performance and the indexability of the dataset, and discuss some heuristics for choosing appropriate DBSCAN parameters. Some indicators of bad parameters will be proposed to help guide future users of this algorithm in choosing parameters such as to obtain both meaningful results and good performance. In new experiments, we show that the new SIGMOD 2015 methods do not appear to offer practical benefits if the DBSCAN parameters are well chosen and thus they are primarily of theoretical interest. In conclusion, the original DBSCAN algorithm with effective indexes and reasonably chosen parameter values performs competitively compared to the method proposed by Gan and Tao.

UCFrame: A Use Case Framework for Crowd-Centric Requirement Acquisition

To build needed mobile applications in specific domains, requirements should be collected and analyzed in holistic approach. However, resource is limited for small vendor groups to perform holistic requirement acquisition and elicitation. The rise of crowdsourcing and crowdfunding gives small vendor groups new opportunities to build needed mobile applications for the crowd. By finding prior stakeholders and gathering requirements effectively from the crowd, mobile application projects can establish sound foundation in early phase of software process. Therefore, integration of crowd-based requirement engineering into software process is important for small vendor groups. Conventional requirement acquisition and elicitation methods are analyst-centric. Very little discussion is in adapting requirement acquisition tools for crowdcentric context. In this study, several tool features of use case documentation are revised in crowd-centric context. These features constitute a use case-based framework, called UCFrame, for crowd-centric requirement acquisition. An instantiation of UCFrame is also presented to demonstrate the effectiveness of UCFrame in collecting crowd requirements for building two mobile applications.

Graphene: efficient interactive set reconciliation applied to blockchain propagation

We introduce Graphene, a method and protocol for interactive set reconciliation among peers in blockchains and related distributed systems. Through the novel combination of a Bloom filter and an Invertible Bloom Lookup Table (IBLT), Graphene uses a fraction of the network bandwidth used by deployed work for one- and two-way synchronization. We show that, for this specific problem, Graphene is more efficient at reconciling n items than using a Bloom filter at the information theoretic bound. We contribute a fast and implementation-independent algorithm for parameterizing an IBLT so that it is optimally small in size and meets a desired decode rate with arbitrarily high probability. We characterize our performance improvements through analysis, detailed simulation, and deployment results for Bitcoin Cash, a prominent cryptocurrency. Our implementations of Graphene, IBLTs, and our IBLT optimization algorithm are all open-source code.

Distributed Spatial and Spatio-Temporal Join on Apache Spark

Effective processing of extremely large volumes of spatial data has led to many organizations employing distributed processing frameworks. Apache Spark is one such open source framework that is enjoying widespread adoption. Within this data space, it is important to note that most of the observational data (i.e., data collected by sensors, either moving or stationary) has a temporal component or timestamp. To perform advanced analytics and gain insights, the temporal component becomes equally important as the spatial and attribute components. In this article, we detail several variants of a spatial join operation that addresses both spatial, temporal, and attribute-based joins. Our spatial join technique differs from other approaches in that it combines spatial, temporal, and attribute predicates in the join operator. In addition, our spatio-temporal join algorithm and implementation differs from others in that it runs in commercial off-the-shelf (COTS) application. The users of this functionality are assumed to be GIS analysts with little if any knowledge of the implementation details of spatio-temporal joins or distributed processing. They are comfortable using simple tools that do not provide the ability to tweak the configuration of the algorithm or processing environment. The spatio-temporal join algorithm behind the tool must always succeed, regardless of input data parameters (e.g., it can be highly irregularly distributed, contain large numbers of coincident points, it can be extremely large, etc.). These factors combine to place additional requirements on the algorithm that are uncommonly found in the traditional research environment. Our spatio-temporal join algorithm was shipped as part of the GeoAnalytics Server [12], part of the ArcGIS Enterprise platform from version 10.5 onward.

3D Printers as Sociable Technologies: Taking Appropriation Infrastructures to the Internet of Things

3D printers have become continuously more present and are a perspicuous example of how technologies are becoming more complex and ubiquitous. To some extent, the emerging technological infrastructures around them exemplify ways how digitalization will change production machines and lines, in general, in the Internet of Things (IoT). From an End-User Development perspective, the main question is how users can be supported in managing those complex digital production lines. To reach a better understanding, we carefully analyzed 3D printers as an example of highly digitalized production machines with regard to the creative activities of their users that help them to make these machines work for their practices. In our study of appropriation processes, we are concerned with situational and social aspects of the configuration and practice challenges associated with making digitalization work and how IoT technologies can support these collaborative appropriation activities of end users by making these machines more “sociable.” We therefore conceptualize the idea of “Sociable Technologies” and implement a prototype that provides hardware-integrated affordances for communicating and documenting practices of usage. Based on the findings of our evaluation, we derive lessons learnt when aiming at making complex technologies more usable.

Designing with Gaze: Tama -- a Gaze Activated Smart-Speaker

Recent developments in gaze tracking present new opportunities for social computing. This paper presents a study of Tama, a gaze actuated smart speaker. Tama was designed taking advantage of research on gaze in conversation. Rather than being activated with a wake word (such as "Ok Google") Tama detects the gaze of a user, moving an articulated 'head' to achieve mutual gaze. We tested Tama's use in a multi-party conversation task, with users successfully activating and receiving a response to over 371 queries (over 10 trials). When Tama worked well, there was no significant difference in length of interaction. However, interactions with Tama had a higher rate of repeated queries, causing longer interactions overall. Video analysis lets us explain the problems users had interacting with gaze. In the discussion, we describe implications for designing new gaze systems, using gaze both as input and output. We also discuss how the relationship to anthropomorphic design and taking advantage of learned skills of interaction. Finally, two paths for future work are proposed, one in the field of speech agents, and the second in using human gaze as an interaction modality more widely.

An arc orienteering algorithm to find the most scenic path on a large-scale road network

Traditional route planning problems mainly focus on finding the shortest path considering the travel distance or time. In this paper, we aim to find the most scenic path that offers the most beautiful sceneries on the arcs of a path while the total travel cost (distance or time) is within a user-specified budget. This is a challenging problem as the optimization objective is to maximize the value of the path (i.e., its scenic value) instead of minimizing its cost (distance or time). The problem can be formulated as a variant of the Arc Orienteering Problem (AOP), which is a well-known NP-hard combinatorial optimization problem. Due to the fast response-time requirements of interactive mobile and online applications (e.g., within 300 milliseconds) and the large scale of real-world road networks, existing heuristic algorithms for AOP fail to solve the most scenic road problem. Therefore, unlike the existing approaches for AOP where they treat the road network as a traditional graph in which all-pair distances are pre-computed a priori, in this work, we treat the road network as a spatial network, utilizing the techniques from the field of spatial database: ellipse pruning and spatial indexing. Experiments on two real-world datasets demonstrate the efficiency and accuracy of our proposed algorithms, which can achieve over 95% accuracy within 300 milliseconds on large-scale datasets (over 100K network nodes).