60,833 research outputs found
Incorporating seasonality into search suggestions derived from intranet query logs
While much research has been performed on query logs collected for major Web search engines, query log analysis to enhance search on smaller and more focused collections has attracted less attention. Our hypothesis is that an intranet search engine can be enhanced by adapting the search system to real usersâ search behaviour through exploiting its query logs. In this work we describe how a constantly adapting domain model can be used to identify and capture changes in intranet usersâ search requirements over time. We employ an algorithm that dynamically builds a domain model from query modifications taken from an intranet query log and employs a decay measure, as used in Machine Learning and Optimisation methods, to promote more recent terms. This model is used to suggest query refinements and additions to users and to elevate seasonally relevant terms. A user evaluation using models constructed from a substantial university intranet query log is provided. Statistical evidence demonstrates the systemâs ability to suggest seasonally relevant terms over three different academic trimesters. We conclude that log files of an intranet search engine are a rich resource to build adaptive domain models, and in our experiments these models significantly outperform sensible baselines
Artificial table testing dynamically adaptive systems
Dynamically Adaptive Systems (DAS) are systems that modify their behavior and
structure in response to changes in their surrounding environment. Critical
mission systems increasingly incorporate adaptation and response to the
environment; examples include disaster relief and space exploration systems.
These systems can be decomposed in two parts: the adaptation policy that
specifies how the system must react according to the environmental changes and
the set of possible variants to reconfigure the system. A major challenge for
testing these systems is the combinatorial explosions of variants and
envi-ronment conditions to which the system must react. In this paper we focus
on testing the adaption policy and propose a strategy for the selection of
envi-ronmental variations that can reveal faults in the policy. Artificial
Shaking Table Testing (ASTT) is a strategy inspired by shaking table testing
(STT), a technique widely used in civil engineering to evaluate building's
structural re-sistance to seismic events. ASTT makes use of artificial
earthquakes that simu-late violent changes in the environmental conditions and
stresses the system adaptation capability. We model the generation of
artificial earthquakes as a search problem in which the goal is to optimize
different types of envi-ronmental variations
Deriving query suggestions for site search
Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T
CYCLOSA: Decentralizing Private Web Search Through SGX-Based Browser Extensions
By regularly querying Web search engines, users (unconsciously) disclose
large amounts of their personal data as part of their search queries, among
which some might reveal sensitive information (e.g. health issues, sexual,
political or religious preferences). Several solutions exist to allow users
querying search engines while improving privacy protection. However, these
solutions suffer from a number of limitations: some are subject to user
re-identification attacks, while others lack scalability or are unable to
provide accurate results. This paper presents CYCLOSA, a secure, scalable and
accurate private Web search solution. CYCLOSA improves security by relying on
trusted execution environments (TEEs) as provided by Intel SGX. Further,
CYCLOSA proposes a novel adaptive privacy protection solution that reduces the
risk of user re- identification. CYCLOSA sends fake queries to the search
engine and dynamically adapts their count according to the sensitivity of the
user query. In addition, CYCLOSA meets scalability as it is fully
decentralized, spreading the load for distributing fake queries among other
nodes. Finally, CYCLOSA achieves accuracy of Web search as it handles the real
query and the fake queries separately, in contrast to other existing solutions
that mix fake and real query results
Design and implementation of a filter engine for semantic web documents
This report describes our project that addresses the challenge of changes in the semantic web. Some studies have already been done for the so-called adaptive semantic web, such as applying inferring rules. In this study, we apply the technology of Event Notification System (ENS). Treating changes as events, we
developed a notification system for such events
A self-adapting latency/power tradeoff model for replicated search engines
For many search settings, distributed/replicated search engines deploy a large number of machines to ensure efficient retrieval. This paper investigates how the power consumption of a replicated search engine can be automatically reduced when the system has low contention, without compromising its efficiency. We propose a novel self-adapting model to analyse the trade-off between latency and power consumption for distributed search engines. When query volumes are high and there is contention for the resources, the model automatically increases the necessary number of active machines in the system to maintain acceptable query response times. On the other hand, when the load of the system is low and the queries can be served easily, the model is able to reduce the number of active machines, leading to power savings. The model bases its decisions on examining the current and historical query loads of the search engine. Our proposal is formulated as a general dynamic decision problem, which can be quickly solved by dynamic programming in response to changing query loads. Thorough experiments are conducted to validate the usefulness of the proposed adaptive model using historical Web search traffic submitted to a commercial search engine. Our results show that our proposed self-adapting model can achieve an energy saving of 33% while only degrading mean query completion time by 10 ms compared to a baseline that provisions replicas based on a previous day's traffic
Searching the intranet: Corporate users and their queries
By examining the log files from a corporate intranet search engine, we have analysed the actual web searching
behaviour of real users in a real business environment. While building on previous research on public search engines, we apply an alternative session definition that we argue is more appropriate. Our results regarding session length, query construction and result page viewing confirm some of the findings from similar studies carried out on public search engines but further our understanding of web searching by presenting details on corporate usersâ activities. In particular, we suggest that search sessions are shorter than previously suggested, search queries have fewer terms than observed for public search engines, and number of examined result pages is smaller than reported in other research. More research on how corporate intranet users search for information is needed
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