Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPersonal Information Databases
202
0
0.0
(
0
)
Added by
R Doomun
Publication date
2009
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
One of the most important aspects of security organization is to establish a framework to identify security significant points where policies and procedures are declared. The (information) security infrastructure comprises entities, processes, and technology. All are participants in handling information, which is the item that needs to be protected. Privacy and security information technology is a critical and unmet need in the management of personal information. This paper proposes concepts and technologies for management of personal information. Two different types of information can be distinguished: personal information and nonpersonal information. Personal information can be either personal identifiable information (PII), or nonidentifiable information (NII). Security, policy, and technical requirements can be based on this distinction. At the conceptual level, PII is defined and formalized by propositions over infons (discrete pieces of information) that specify transformations in PII and NII. PII is categorized into simple infons that reflect the proprietor s aspects, relationships with objects, and relationships with other proprietors. The proprietor is the identified person about whom the information is communicated. The paper proposes a database organization that focuses on the PII spheres of proprietors. At the design level, the paper describes databases of personal identifiable information built exclusively for this type of information, with their own conceptual scheme, system management, and physical structure.
rate research
Read More
Personal Information Management (PIM) refers to the practice and the study of the activities a person performs in order to acquire or create, store, organize, maintain, retrieve, use, and distribute information in each of its many forms (paper and digital, in e-mails, files, Web pages, text messages, tweets, posts, etc.) as needed to meet lifes many goals (everyday and long-term, work-related and not) and to fulfill lifes many roles and responsibilities (as parent, spouse, friend, employee, member of community, etc.). PIM activities are an effort to establish, use, and maintain a mapping between information and need. Activities of finding (and re-finding) move from a current need toward information while activities of keeping move from encountered information toward anticipated need. Meta-level activities such as maintaining, organizing, and managing the flow of information focus on the mapping itself. Tools and techniques of PIM can promote information integration with benefits for each kind of PIM activity and across the life cycle of personal information. Understanding how best to accomplish this integration without inadvertently creating problems along the way is a key challenge of PIM.
This paper addresses the problem of representing the set of repairs of a possibly inconsistent database by means of a disjunctive database. Specifically, the class of denial constraints is considered. We show that, given a database and a set of denial constraints, there exists a (unique) disjunctive database, called canonical, which represents the repairs of the database w.r.t. the constraints and is contained in any other disjunctive database with the same set of minimal models. We propose an algorithm for computing the canonical disjunctive database. Finally, we study the size of the canonical disjunctive database in the presence of functional dependencies for both repairs and cardinality-based repairs.
NoSQL databases like Redis, Cassandra, and MongoDB are increasingly popular because they are flexible, lightweight, and easy to work with. Applications that use these databases will evolve over time, sometimes necessitating (or preferring) a change to the format or organization of the data. The problem we address in this paper is: How can we support the evolution of high-availability applications and their NoSQL data online, without excessive delays or interruptions, even in the presence of backward-incompatible data format changes? We present KVolve, an extension to the popular Redis NoSQL database, as a solution to this problem. KVolve permits a developer to submit an upgrade specification that defines how to transform existing data to the newest version. This transformation is applied lazily as applications interact with the database, thus avoiding long pause times. We demonstrate that KVolve is expressive enough to support substantial practical updates, including format changes to RedisFS, a Redis-backed file system, while imposing essentially no overhead in general use and minimal pause times during updates.
In this work, we track the lineage of tuples throughout their database lifetime. That is, we consider a scenario in which tuples (records) that are produced by a query may affect other tuple insertions into the DB, as part of a normal workflow. As time goes on, exact provenance explanations for such tuples become deeply nested, increasingly consuming space, and resulting in decreased clarity and readability. We present a novel approach for approximating lineage tracking, using a Machine Learning (ML) and Natural Language Processing (NLP) technique; namely, word embedding. The basic idea is summarizing (and approximating) the lineage of each tuple via a small set of constant-size vectors (the number of vectors per-tuple is a hyperparameter). Therefore, our solution does not suffer from space complexity blow-up over time, and it naturally ranks explanations to the existence of a tuple. We devise an alternative and improved lineage tracking mechanism, that of keeping track of and querying lineage at the column level; thereby, we manage to better distinguish between the provenance features and the textual characteristics of a tuple. We integrate our lineage computations into the PostgreSQL system via an extension (ProvSQL) and experimentally exhibit useful results in terms of accuracy against exact, semiring-based, justifications. In the experiments, we focus on tuples with multiple generations of tuples in their lifelong lineage and analyze them in terms of direct and distant lineage. The experiments suggest a high usefulness potential for the proposed approximate lineage methods and the further suggested enhancements. This especially holds for the column-based vectors method which exhibits high precision and high per-level recall.
Graph query languages feature mainly two kinds of queries when applied to a graph database: those inspired by relational databases which return tables such as SELECT queries and those which return graphs such as CONSTRUCT queries in SPARQL. The latter are object of study in the present paper. For this purpose, a core graph query language GrAL is defined with focus on CONSTRUCT queries. Queries in GrAL form the final step of a recursive process involving so-called GrAL patterns. By evaluating a query over a graph one gets a graph, while by evaluating a pattern over a graph one gets a set of matches which involves both a graph and a table. CONSTRUCT queries are based on CONSTRUCT patterns, and sub-CONSTRUCT patterns come for free from the recursive definition of patterns. The semantics of GrAL is based on RDF graphs with a slight modification which consists in accepting isolated nodes. Such an extension of RDF graphs eases the definition of the evaluation semantics, which is mainly captured by a unique operation called Merge. Besides, we define aggregations as part of GrAL expressions, which leads to an original local processing of aggregations.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
