2,721 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
A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data
We describe a generic framework for representing and reasoning with annotated
Semantic Web data, a task becoming more important with the recent increased
amount of inconsistent and non-reliable meta-data on the web. We formalise the
annotated language, the corresponding deductive system and address the query
answering problem. Previous contributions on specific RDF annotation domains
are encompassed by our unified reasoning formalism as we show by instantiating
it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we
provide a generic method for combining multiple annotation domains allowing to
represent, e.g. temporally-annotated fuzzy RDF. Furthermore, we address the
development of a query language -- AnQL -- that is inspired by SPARQL,
including several features of SPARQL 1.1 (subqueries, aggregates, assignment,
solution modifiers) along with the formal definitions of their semantics
A Survey on Important Aspects of Information Retrieval
Information retrieval has become an important field of study and research under computer science due to the explosive growth of information available in the form of full text, hypertext, administrative text, directory, numeric or bibliographic text. The research work is going on various aspects of information retrieval systems so as to improve its efficiency and reliability. This paper presents a comprehensive survey discussing not only the emergence and evolution of information retrieval but also include different information retrieval models and some important aspects such as document representation, similarity measure and query expansion
Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
The emergence of natural language processing has revolutionized the way users
interact with tabular data, enabling a shift from traditional query languages
and manual plotting to more intuitive, language-based interfaces. The rise of
large language models (LLMs) such as ChatGPT and its successors has further
advanced this field, opening new avenues for natural language processing
techniques. This survey presents a comprehensive overview of natural language
interfaces for tabular data querying and visualization, which allow users to
interact with data using natural language queries. We introduce the fundamental
concepts and techniques underlying these interfaces with a particular emphasis
on semantic parsing, the key technology facilitating the translation from
natural language to SQL queries or data visualization commands. We then delve
into the recent advancements in Text-to-SQL and Text-to-Vis problems from the
perspectives of datasets, methodologies, metrics, and system designs. This
includes a deep dive into the influence of LLMs, highlighting their strengths,
limitations, and potential for future improvements. Through this survey, we aim
to provide a roadmap for researchers and practitioners interested in developing
and applying natural language interfaces for data interaction in the era of
large language models.Comment: 20 pages, 4 figures, 5 tables. Submitted to IEEE TKD
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