scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

Practice Makes Perfect: Lesson Learned from Five Years of Trial and Error Building Context-Aware Systems

TL;DR: Three lessons learned from the past five years of developing context-aware systems are presented that are believed to improve future system design and influence human-machine teaming.
Abstract: Recent advances in artificial intelligence have demonstrated that the future of work will be defined by collaborative human-machine teams. In order to be effective, human-machine teams will rely on context-aware systems to enable collaboration. In this paper, we present three lessons learned from the past five years of developing context-aware systems that we believe will improve future system design. First, that semantic activity must captured, modeled, and analyzed to enable reasoning across missions, actors, and content. Second, that context-aware systems require multiple, federated data stores to optimize system and team performance. Finally, that real-time inter-actor communications are the essential feature enabling adaptation. We close with a discussion of the influences and implications that these lessons have on human-machine teaming, and outline future research activities that will be necessary before operationalizing
Citations
More filters
Journal ArticleDOI
01 Dec 2020
TL;DR: In this article, cyber reasoning systems demonstrated near-human performance characteristics when they autonomously identified, proved, and mitigated vulnerabilities in software during a competitive event and demonstrated near human performance characteristics.
Abstract: Recently, cyber reasoning systems demonstrated near-human performance characteristics when they autonomously identified, proved, and mitigated vulnerabilities in software during a competitive event...
References
More filters
Book
01 Jan 2014
TL;DR: An Invitation to Grounded Theory Gathering Rich Data Crafting and Conducting Intensive Interviews Interviewing in Grounded theory Studies The logic of grounded theory Coding Practices and Initial Coding Focused Coding and beyond Memo-Writing Theoretical Sampling, Saturation and Sorting Reconstructing theory in grounded theories as mentioned in this paper.
Abstract: An Invitation to Grounded Theory Gathering Rich Data Crafting and Conducting Intensive Interviews Interviewing in Grounded Theory Studies The Logic of Grounded Theory Coding Practices and Initial Coding Focused Coding and beyond Memo-Writing Theoretical Sampling, Saturation and Sorting Reconstructing Theory in Grounded Theory Studies Symbolic Interactionism and Grounded Theory Writing the Draft Reflecting on the Research Process

9,120 citations

Proceedings ArticleDOI
01 Apr 2001
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Abstract: Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative ltering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative ltering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between di erent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze di erent item-based recommendation generation algorithms. We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.

8,634 citations


"Practice Makes Perfect: Lesson Lear..." refers background or methods in this paper

  • ...Typical solutions leverage collaborative filtering [19] to predict relevance....

    [...]

  • ...Consider commercial recommender algorithms used by Netflix [13], Hulu [14], and Amazon [15], which treat context as a series of sparse vectors that can be isolated and assessed for similarity [19]....

    [...]

Journal Article
TL;DR: This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.
Abstract: Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.

4,788 citations


"Practice Makes Perfect: Lesson Lear..." refers background or methods in this paper

  • ...adaptive user interfaces [9, 10], to knowledge bases [11, 12], to recommendation engines [13, 14, 15]....

    [...]

  • ...Consider commercial recommender algorithms used by Netflix [13], Hulu [14], and Amazon [15], which treat context as a series of sparse vectors that can be isolated and assessed for similarity [19]....

    [...]

Journal ArticleDOI
TL;DR: A conceptual framework is presented that separates the acquisition and representation of context from the delivery and reaction to context by a context-aware application, and a toolkit is built that instantiates this conceptual framework and supports the rapid development of a rich space of context- aware applications.
Abstract: Computing devices and applications are now used beyond the desktop, in diverse environments, and this trend toward ubiquitous computing is accelerating. One challenge that remains in this emerging research field is the ability to enhance the behavior of any application by informing it of the context of its use. By context, we refer to any information that characterizes a situation related to the interaction between humans, applications, and the surrounding environment. Context-aware applications promise richer and easier interaction, but the current state of research in this field is still far removed from that vision. This is due to 3 main problems: (a) the notion of context is still ill defined, (b) there is a lack of conceptual models and methods to help drive the design of context-aware applications, and (c) no tools are available to jump-start the development of context-aware applications. In this anchor article, we address these 3 problems in turn. We first define context, identify categories of contextual information, and characterize context-aware application behavior. Though the full impact of context-aware computing requires understanding very subtle and high-level notions of context, we are focusing our efforts on the pieces of context that can be inferred automatically from sensors in a physical environment. We then present a conceptual framework that separates the acquisition and representation of context from the delivery and reaction to context by a context-aware application. We have built a toolkit, the Context Toolkit, that instantiates this conceptual framework and supports the rapid development of a rich space of context-aware applications. We illustrate the usefulness of the conceptual framework by describing a number of context-aware applications that have been prototyped using the Context Toolkit. We also demonstrate how such a framework can support the investigation of important research challenges in the area of context-aware computing.

3,095 citations


"Practice Makes Perfect: Lesson Lear..." refers background in this paper

  • ...A commonly used definition of context is ”any information that characterizes a situation related to the interaction between humans, applications and the surrounding environment” [23]....

    [...]

Book
01 Jul 1997
TL;DR: This book introduces a customer-centered approach to business by showing how data gathered from people while they work can drive the definition of a product or process while supporting the needs of teams and their organizations.
Abstract: This book introduces a customer-centered approach to business by showing how data gathered from people while they work can drive the definition of a product or process while supporting the needs of teams and their organizations. This is a practical, hands-on guide for anyone trying to design systems that reflect the way customers want to do their work. The authors developed Contextual Design, the method discussed here, through their work with teams struggling to design products and internal systems. In this book, you'll find the underlying principles of the method and how to apply them to different problems, constraints, and organizational situations. Contextual Design enables you to + gather detailed data about how people work and use systems + develop a coherent picture of a whole customer population + generate systems designs from a knowledge of customer work + diagram a set of existing systems, showing their relationships, inconsistencies, redundancies, and omissions Table of Contents Chapter 1 Introduction Chapter 2 Gathering Customer Data Chapter 3 Principles of Contextual Inquiry Chapter 4 Contextual Inquiry in Practice Chapter 5 A Language of Work Chapter 6 Work Models Chapter 7 The Interpretation Session Chapter 8 Consolidation Chapter 9 Creating One View of the Customer Chapter 10 Communicating to the Organization Chapter 11 Work Redesign Chapter 12 Using Data to Drive Design Chapter 13 Design from Data Chapter 14 System Design Chapter 15 The User Environment Design Chapter 16 Project Planning and Strategy Chapter 17 Prototyping as a Design Tool Chapter 18 From Structure to User Interface Chapter 19 Iterating with a Prototype Chapter 20 Putting It into Practice

2,945 citations


"Practice Makes Perfect: Lesson Lear..." refers methods in this paper

  • ...Each application began with domain analysis using techniques such as hybrid Cognitive Task Analysis [30] and Contextual Design [31]....

    [...]