14,351 research outputs found
SoK: Cryptographically Protected Database Search
Protected database search systems cryptographically isolate the roles of
reading from, writing to, and administering the database. This separation
limits unnecessary administrator access and protects data in the case of system
breaches. Since protected search was introduced in 2000, the area has grown
rapidly; systems are offered by academia, start-ups, and established companies.
However, there is no best protected search system or set of techniques.
Design of such systems is a balancing act between security, functionality,
performance, and usability. This challenge is made more difficult by ongoing
database specialization, as some users will want the functionality of SQL,
NoSQL, or NewSQL databases. This database evolution will continue, and the
protected search community should be able to quickly provide functionality
consistent with newly invented databases.
At the same time, the community must accurately and clearly characterize the
tradeoffs between different approaches. To address these challenges, we provide
the following contributions:
1) An identification of the important primitive operations across database
paradigms. We find there are a small number of base operations that can be used
and combined to support a large number of database paradigms.
2) An evaluation of the current state of protected search systems in
implementing these base operations. This evaluation describes the main
approaches and tradeoffs for each base operation. Furthermore, it puts
protected search in the context of unprotected search, identifying key gaps in
functionality.
3) An analysis of attacks against protected search for different base
queries.
4) A roadmap and tools for transforming a protected search system into a
protected database, including an open-source performance evaluation platform
and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac
Semantic web technology to support learning about the semantic web
This paper describes ASPL, an Advanced Semantic Platform for Learning, designed using the Magpie framework with an aim to support students learning about the Semantic Web research area. We describe the evolution of ASPL and illustrate how we used the results from a formal evaluation of the initial system to re-design the user functionalities. The second version of ASPL semantically interprets the results provided by a non-semantic web mining tool and uses them to support various forms of semantics-assisted exploration, based on pedagogical strategies such as performing later reasoning steps and problem space filtering
Symbolic Computing with Incremental Mindmaps to Manage and Mine Data Streams - Some Applications
In our understanding, a mind-map is an adaptive engine that basically works
incrementally on the fundament of existing transactional streams. Generally,
mind-maps consist of symbolic cells that are connected with each other and that
become either stronger or weaker depending on the transactional stream. Based
on the underlying biologic principle, these symbolic cells and their
connections as well may adaptively survive or die, forming different cell
agglomerates of arbitrary size. In this work, we intend to prove mind-maps'
eligibility following diverse application scenarios, for example being an
underlying management system to represent normal and abnormal traffic behaviour
in computer networks, supporting the detection of the user behaviour within
search engines, or being a hidden communication layer for natural language
interaction.Comment: 4 pages; 4 figure
Knowledge Representation with Ontologies: The Present and Future
Recently, we have seen an explosion of interest in ontologies as
artifacts to represent human knowledge and as critical components in
knowledge management, the semantic Web, business-to-business
applications, and several other application areas. Various research
communities commonly assume that ontologies are the appropriate modeling
structure for representing knowledge. However, little discussion has
occurred regarding the actual range of knowledge an ontology can
successfully represent
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A Double Error Dynamic Asymptote Model of Associative Learning
In this paper a formal model of associative learning is presented which incorporates representational and computational mechanisms that, as a coherent corpus, empower it to make accurate predictions of a wide variety of phenomena that so far have eluded a unified account in learning theory. In particular, the Double Error Dynamic Asymptote (DDA) model introduces: 1) a fully-connected network architecture in which stimuli are represented as temporally clustered elements that associate to each other, so that elements of one cluster engender activity on other clusters, which naturally implements neutral stimuli associations and mediated learning; 2) a predictor error term within the traditional error correction rule (the double error), which reduces the rate of learning for expected predictors; 3) a revaluation associability rate that operates on the assumption that the outcome predictiveness is tracked over time so that prolonged uncertainty is learned, reducing the levels of attention to initially surprising outcomes; and critically 4) a biologically plausible variable asymptote, which encapsulates the principle of Hebbian learning, leading to stronger associations for similar levels of cluster activity. The outputs of a set of simulations of the DDA model are presented along with empirical results from the literature. Finally, the predictive scope of the model is discussed
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
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