Qualified Selves: Co-Creating Meaning Post-Big Data

个人越来越依赖数字应用程序和服务来存储照片、文档、笔记和其他有价值的个人数据。他们还习惯于--并默许为使用其他“免费”服务的隐藏成本--这些应用程序收集活动数据和元数据，公司从这些数据和元数据中获得重大商业价值。例如，Facebook的年收入高达220亿GB，通过破译用户的喜好、标签、联系人、更新、照片、旅行模式等(其中一些是通过其他应用程序访问Facebook的权限)，能够精确地向用户投放广告，从而获得了很大一部分收入。有一种利润丰厚的用户数据交易：Acxiom和Epsilon等数据经纪公司收集关于人们的身心健康状况、性取向、个人恶习和脆弱性的详尽档案，以帮助公司识别潜在消费者[CBS，SCH]。与此同时，没有相应的工具可供个人通过个人数据了解自己。近年来，人们越来越多地将数据作为产生信息和关键见解的媒介。这体现在量化的自我运动中(例如，参见：http://feltron.com)，)，个体自我跟踪他们的行为模式、生理反应、生产力、通信等，以期实现个人反思和获得更大的自我认识[Li]。可穿戴活动跟踪器已经被一些人用于自我诊断目的：例如，寻找活动和症状之间的关联，以做出知情的改变，以改善个人福祉[Roo]。将这种感觉应用于更广泛、更深层次的个人感知方面，通过将个人数据的多样性与各种服务和应用程序-从广泛的网络服务，到移动应用程序、可穿戴设备和家用物联网设备-之间建立联系，具有尚未开发的潜力。个人信息管理(PIM)是一个不断增长的ICT部门，估计市场价值为165亿GB[NES]。PIM专注于四个主要活动--保存、查找、组织和维护--为如何开发和维持有效管理自己的数据的实践提供了宝贵的见解[Kli]。开发PIM解决方案的一个特殊挑战是非专业数据管理技术和策略的个性，这些技术和策略映射到人们的个人优势和熟悉的、既定的实践；简而言之，当他们能够制定为他们工作并为他们定义的特定目标工作的战略时，个人就会茁壮成长。鉴于许多服务表面上向用户提供信息管理(尽管有预先设置的UX约束)，扩展PIM研究的一个特别有趣的前沿在于从当前管理它们的应用程序中取出数据，以支持个性化的、面向目标的个人数据收集和管理，并进一步提供在不同数据类型(例如，元数据、叙事/文本数据、照片数据、活动数据等的细节)之间进行管理的技术。该项目将填补对个人意义理解的几个重要空白，包括：1)与提取、收集和分析人们的个人数据的商业目的相反，了解哪些类型的自我知识将为个人提供重要价值，以及如何在应用程序和服务之间连接个人数据可能独特地提供这些个人洞察；以及2)了解人们如何从混合数据类型和跨应用程序中获得意义，而不受他们用来捕获个人数据的单个应用程序或服务的目标导向的限制。

Individuals are increasingly reliant on digital applications and services to store photos, documents, notes and other valued personal data. They are also accustomed to - and tacitly accept as a hidden cost of using otherwise 'free' services - these applications amassing activity data and metadata from which companies derive significant business value. For example, Facebook makes much of its £22bn yearly revenue by being able to precisely target advertisements to users by deciphering their unique preferences from their likes, tags, contacts, updates, photos, travel patterns etc (some accessed through permissions to Facebook via other apps) [BBC]. There is a highly lucrative, if shadowy, trafficking in users' data: data brokering companies such as Acxiom and Epsilon compile thorough dossiers on people's physical and mental health conditions, sexual orientation, personal vices, and vulnerabilities to aid companies in identifying likely consumers [CBS,SCH]. Meanwhile, there are no corresponding tools accessible for individuals to learn about themselves through their personal data. Within recent years, there is a growing literacy around data as a medium for generating information and key insights. This is represented in the Quantified Self movement (see, e.g.: http://feltron.com), with individuals self-tracking their patterns of behaviour, physiological responses, productivity, correspondences etc with a view toward enabling personal reflection and gaining greater self-knowledge [LI]. Wearable activity trackers have been appropriated by some for self-diagnostic purposes: e.g. finding correlations between activities and symptoms to make informed changes to improve personal wellbeing [ROO]. There is untapped potential in applying this sensibility toward broader and deeper personal sense-making by drawing connections between the full diversity of one's personal data currently siloed in various services and applications - from the wide array of web services, to mobile applications, wearable and home IoT devices.Personal Information Management (PIM) is a growing ICT sector with an estimated market worth of £16.5bn [NES]. Focusing on four major activities - keeping, finding, organizing and maintaining - PIM offers valuable insights into how to develop and sustain practices for effectively managing one's own data [KLI]. A particular challenge in developing PIM solutions is the individuality of lay data management techniques and strategies, which map onto people's individual strengths and familiar, established practices; in short, individuals thrive when they are able to develop strategies that work for them and for the particular goals they have defined. Given that many services ostensibly offer information management to users (albeit with pre-set UX constraints), an especially interesting frontier for extending PIM research lies in lifting data out from the applications that are currently managing them to support individualised, goal oriented collection and management of personal data - and further, offering techniques for managing between diverse data types (e.g. the minutia of metadata, narrative/textual data, photographic data, activity data, etc).This project will fill several important gaps in understandings of personal sense-making, including: 1) in contrast to commercial ends for extracting, collecting and analysing people's personal data, understanding what kinds of self-knowledge would offer significant value to individuals, and how bridging personal data between applications and services might uniquely afford these personal insights; and 2) understanding how people can derive meaning from mixed data types and across applications, unbounded by the goal orientations of the individual applications or services they use to capture their personal data.