41,864 research outputs found
A study of remembered context for information access from personal digital archives
Retrieval from personal archives (or Human Digital Memories
(HDMs)) is set to become a significant challenge in information retrieval (IR) research. These archives are unique in that the items in them are personal to the owner and as such the owner may have personal memories associated
with the items. It is recognized that the harnessing of an
individualās memories about HDM items can be used as context
data (such as user location at the time of item access) to aid retrieval. We present a pilot study, using one subjectās
HDM, of remembered context data and its utility in retrieval. Our results explore the types of context data best remembered for different item types and categories over time and show that context appears to become a more important factor in effective HDM IR over time as the subjectās recall of contents declines
Memory support for desktop search
The user's memory plays a very important role in desktop
search. A search query with insufficiently or inaccurately
recalled information may make the search dramatically less
effective. In this paper, we discuss three approaches to support userās memory during desktop search. These include extended types of well remembered search options, the use of past search queries and results, and search from similar items. We will also introduce our search system which incorporates these features
Information access tasks and evaluation for personal lifelogs
Emerging personal lifelog (PL) collections contain permanent digital records of information associated with individualsā daily lives. This can include materials such as emails received and sent, web content and other documents with which they have interacted, photographs, videos and music experienced passively or created, logs of phone calls and text messages, and also personal and contextual data such as location (e.g. via GPS sensors), persons and objects present (e.g. via Bluetooth) and physiological state (e.g. via biometric sensors). PLs can be collected by individuals over very extended periods, potentially running to many years. Such archives have many potential applications including helping individuals recover partial forgotten information, sharing experiences with friends or family, telling the story of oneās life, clinical applications for the memory impaired, and fundamental psychological investigations of memory. The Centre for Digital Video Processing (CDVP) at Dublin City University is currently engaged in the collection and exploration of applications of large PLs. We are collecting rich archives of daily life including textual and visual materials, and contextual context data. An important part of this work is to consider how the effectiveness of our ideas can be measured in terms of metrics and experimental design. While these studies have considerable similarity with traditional evaluation activities in areas such as information retrieval and summarization, the characteristics of PLs mean that new challenges and questions emerge. We are currently exploring the issues through a series of pilot studies and questionnaires. Our initial results indicate that there are many research questions to be explored and that the relationships between personal memory, context and content for these tasks is complex and fascinating
Exploring Memory Cues to Aid Information Retrieval from Personal LifeLog Archives
The expansion of personal information archives and the emerging field of Personal Lifelogs (PLs) are creating new challenges for information retrieval (IR). While studies have demonstrated the difficulties of IR for these massive data collection [1], we should also think about how we can opportunities and benefits from integrating these data sources as a component of ādigital memoriesā , considering their rich connections with the usersā memory. We observed that most existing approaches to personal archive IR are mostly technology-driven. Although in recent years studies in Personal Information management (PIM) have claimed to make use of the human memory features, and many works have been reported as investigating well-remembered features of computer files (documents, email, photos). Yet, these explorations are usually confined to the attributes or feature that current computer file systems or technology have provided.
I believe that there are important and potentially useful data attributes that these studies have ignored. In addition, current personal search interfaces provide searching options based on what is available in the system, e.g. require users to fill in the calendar date, regardless of the fact that people actually donāt often encode ātimeā in such a way. My PhD project aims to explore what users actually tend to recall in different personal achieve information seeking tasks, how to present searching options to cater for the right type or format of information that users can recall, and how to exploit this information in an IR system for personal lifelog archives.
In this paper, I discuss the limits and advantages of some related work, and present my current and proposed study, with an outlook of an interface that I plan to develop to explore my proposals
Integrating memory context into personal information re-finding
Personal information archives are emerging as a new challenge for information retrieval (IR) techniques.
The userās memory plays a greater role in retrieval from person archives than from other more traditional types of information collection (e.g. the Web), due to the large overlap of its content and individual human memory of the captured material. This paper presents a new analysis on IR of personal archives from a cognitive perspective. Some existing work on personal information management (PIM) has begun to employ human memory features into their IR systems. In our work we seek to go further, we assume that for IR in PIM system terms can be weighted not only by traditional IR methods, but also taking the userās recall reliability into account. We aim to develop algorithms that
combine factors from both the system side and the user side to achieve more effective searching. In this paper, we discuss possible applications of human memory theories for this algorithm, and present results from a pilot study and a proposed model of data structure for the HDMs achieves
Augmenting human memory using personal lifelogs
Memory is a key human facility to support life activities, including social interactions, life management and problem solving. Unfortunately, our memory is not perfect. Normal individuals will have occasional memory problems which can be frustrating, while those with memory impairments can often experience a greatly reduced quality of life. Augmenting memory has the potential to make normal individuals more effective, and those with significant memory problems to have a higher general quality of life. Current technologies are now making it possible to automatically capture and store daily life experiences over an extended period, potentially even over a lifetime. This type of data collection, often referred to as a personal life log (PLL), can include data such as continuously captured pictures or videos from a first person perspective, scanned copies of archival material such as books, electronic documents read or created, and emails and SMS messages sent and received, along with context data of time of capture and access and location via GPS sensors.
PLLs offer the potential for memory augmentation. Existing work on PLLs has focused on the technologies of data capture and retrieval, but little work has been done to explore how these captured data and retrieval techniques can be applied to actual use by normal people in supporting their memory. In this paper, we explore the needs for augmenting human memory from normal people based on the psychology literature on mechanisms about memory problems, and discuss the possible functions that PLLs can provide to support these memory augmentation needs. Based on this, we also suggest guidelines for data for capture, retrieval needs and computer-based interface design. Finally we introduce our work-in-process prototype PLL search system in the iCLIPS project to give an example of augmenting human memory with PLLs and computer based interfaces
Examining the utility of affective response in search of personal lifelogs
Personal lifelog archives contain digital records captured from an individualās daily life, for example emails, documents edited, webpages downloaded and photographs taken. While capturing this information is becoming increasingly easy, subsequently locating interesting items from within these archives is a significant challenge. One potential source of information to identify items of importance to an individual is their affective state during the capture of the information. The strength of an individualās affective response to their current situation can often be gauged from their physiological response. For this study we explored the utility of the following biometric features to indicate significant items:
galvanic skin response (GSR), heart rate (HR) and skin temperature (ST). Significant or important events tend to raise an individualās arousal level, causing a measurable biometric response. We examined the utility of using biometric response to identify significant items and for re-ranking traditional information retrieval (IR) result sets. Results obtained indicate that skin temperature is most useful for extracting interesting items from personal archives containing passively captured images, computer activity and SMS messages
The information retrieval challenge of human digital memories
Today people are storing increasing amounts of personal information in digital format. While storage of such
information is becoming straight forward, retrieval from the vast personal archives that this is creating poses
significant challenges. Existing retrieval techniques are good at retrieving from non-personal spaces, such as the
World Wide Web. However they are not sufficient for retrieval of items from these new unstructured spaces
which contain items that are personal to the individual, and of which the user has personal memories and with
which has had previous interaction. We believe that there are new and exciting possibilities for retrieval from
personal archives. Memory cues act as triggers for individuals in the remembering process, a better
understanding of memory cues will enable us to design new and effective retrieval algorithms and systems for
personal archives. Context data, such as time and location, is already proving to play a key part in this special
retrieval domain, for example for searching personal photo archives, we believe there are many other rich
sources of context that can be exploited for retrieval from personal archives
Applying contextual memory cues for retrieval from personal information archives
Advances in digital technologies for information capture
combined with massive increases in the capacity of digital
storage media mean that it is now possible to capture and store oneās entire life experiences in a Human Digital Memory (HDM). Information can be captured from a myriad of personal information devices including desktop computers, PDAs, digital cameras, video and audio recorders, and various sensors, including GPS, Bluetooth, and biometric devices. These diverse collections of personal information are potentially very valuable, but will only be so if significant information can be reliably retrieved from them. HDMs differ from traditional document collections for which existing search technologies have been developed since users may have poor recollection of contents or even the existence of stored items. Additionally HDM data is highly heterogeneous and unstructured, making it difficult to form search queries. We believe that a Personal Information Management (PIM) system which exploits the context of information capture, and potentially of earlier refinding, can be valuable in effective retrieval from an
HDM. We report an investigation into how individuals
perform searches of their personal information, and use
the outcome of this study to develop an information retrieval (IR) framework for HDM search incorporating the context of document capture. We then describe the creation of a pilot HDM test collection, and initial experiments in retrieval from this collection. Results from these experiments indicate that use of context data can be significantly beneficial to increasing the efficient retrieval of partially recalled items from an HDM
Challenges and opportunities of context-aware information access
Ubiquitous computing environments embedding a wide range of pervasive computing technologies provide a challenging and exciting new domain for information access. Individuals working in these environments are increasingly permanently connected to rich information resources. An appealing opportunity of these environments is the potential to deliver useful information to individuals either from their previous information experiences or external sources. This information should enrich their life experiences or make them more effective in their endeavours. Information access in ubiquitous computing environments can be made "context-aware" by exploiting the wide range context data available describing the environment, the searcher and the information itself. Realizing such a vision of reliable, timely and appropriate identification and delivery of information in this way poses numerous challenges. A central theme in achieving context-aware information access is the combination of information retrieval with multiple dimensions of available context data. Potential context data sources, include the user's current task, inputs from environmental and biometric sensors, associated with the user's current context, previous contexts, and document context, which can be exploited using a variety of technologies to create new and exciting possibilities for information access
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