We propose to demonstrate an open-source tool, LinkIT, for privacy preserving record Linkage and Integration via data Transformations. LinkIT implements novel algorithms that support data transformations for linking sensitive attributes, and is designed to work with our previously developed tool, FRIL (Fine-grained Record Integration and Linkage), to provide a complete record linkage solution. LinkIT can be also used as a stand-alone secure transformation tool to link string records. The system uses a novel embedding technique based on frequent variable length grams mined from original records with differential privacy, and utilizes a personalized threshold for performing linkage in the embedded space. Compared to the state-of-the-art secure transformation method [16], LinkIT guarantees stronger privacy with better scalability while achieving comparable utility results.

While current biomedical ontology repositories offer primitive query capabilities, it is difficult or cumbersome to support ontology based semantic queries directly in semantically annotated biomedical databases. The problem may be largely attributed to the mismatch between the models of the ontologies and the databases, and the mismatch between the query interfaces of the two systems. To fully realize semantic query capabilities based on ontologies, we develop a system DBOntoLink to provide unified semantic query interfaces by extending database query languages. With DBOntoLink, semantic queries can be directly and naturally specified as extended functions of the database query languages without any programming needed. DBOntoLink is adaptable to different ontologies through customizations and supports major biomedical ontologies hosted at the NCBO BioPortal. We demonstrate the use of DBOntoLink in a real world biomedical database with semantically annotated medical image annotations.

Constraint satisfaction problems (CSP) are often posed over data residing in relational database systems, which serve as passive data-storage back ends. Several studies have demonstrated a number of important advantages to having database systems capable of natively modelling and solving CSPs. This paper studies the automated decomposition of the input CSP, borrowing another distinctive idea of relational databases, normalization, to improve its solution time. Experimental evaluations for two case studies show the potential benefit of the approach.

While there is an increasing need to share data that may contain personal information, such data sharing must preserve individual privacy without disclosing any identifiable information. A considerable amount of research in the data privacy community has been devoted to formalizing the notion of identifiability with many techniques for anonymization, but is focused exclusively on structured data. On the other hand, efforts on de-identifying medical text documents in the medical informatics community are highly specialized for specific document types or a subset of identifiers. In addition, they rely on simple identifier removal or grouping techniques and do not take advantage of the research developments in the data privacy community. We developed an integrated system, HIDE, for Heterogeneous Information DE-identification including structured and unstructured data utilizing existing anonymization techniques. We demonstrate a prototype of our system and show the effectiveness of our approach through a set of real data augmented with synthesized data.

Jeannette Wing's call for teaching Computational Thinking (CT) as a formative skill on par with reading, writing, and arithmetic places computer science in the category of basic knowledge. Just as proficiency in basic language arts helps us to effectively communicate and in basic math helps us to successfully quantitate, proficiency in computational thinking helps us to systematically and efficiently process information and tasks. But while teaching everyone to think computationally is a noble goal, there are pedagogical challenges. Perhaps the most confounding issue is the role of programming, and whether we can separate it from teaching basic computer science. How much programming, if any, should be required for CT proficiency?

Flow cytometry technique produces large, multi-dimensional datasets of properties of individual cells that are helpful for biomedical science and clinical research. This paper explores an approach for comparing and clustering flow cytometry data. To overcome challenges posed by the irregularities and the high dimensions of the data, we develop a set of data preprocessing techniques to facilitate effective clustering of flow cytometry data files. We present a set of experiments using real data from the Protective Immunity Project (PIP) showing the effectiveness of the approach.

A new technique for improving the efficiency of propositional reasoning procedures is presented. The meta-search procedure, ND, is parameterized by a search procedure P and a real number for controlling the way in which P is applied to the given problem. Experiments using SATO on the domain of Crossword Puzzle Construction (CPC) illustrate the potential for ND. Variations of and future experiments with ND are discussed.

Although there are many applications where an object-oriented data model is a good way of representing and querying data, current object database systems are unable to handle objects whose attributes are uncertain. In this article, we extend previous work by Kornatzky and Shimony to develop an algebra to handle object bases with uncertainty. We propose concepts of consistency for such object bases, together with an NP-completeness result, and classes of probabilistic object bases for which consistency is polynomially checkable. In addition, as certain operations involve conjunctions and disjunctions of events, and as the probability of conjunctive and disjunctive events depends both on the probabilities of the primitive events involved as well as on what is known (if anything) about the relationship between the events, we show how all our algebraic operations may be performed under arbitrary probabilistic conjunction and disjunction strategies. We also develop a host of equivalence results in our algebra, which may be used as rewrite rules for query optimization. Last but not least, we have developed a prototype probabilistic object base server on top of ObjectStore. We describe experiments to assess the efficiency of different possible rewrite rules.

Signed formula is a formalism that has been applied to reasoning about multiple-valued logics. In this paper, the theory of logic programming based on signed formula is developed, and its connection to annotated logic programming investigated. It is shown that a signed formula logic program, together with annotated logic, forms a paraconsistent basis for reasoning about ‘inconsistent’multiplevalued logic programs. A query processing procedure based on signed resolution is introduced.

Deductive databases that interact with, and are accessed by, reasoning agents in the real world (such as logic controllers in automated manufacturing, weapons guidance systems, aircraft landing systems, land-vehicle maneuvering systems, and air-traffic control systems) must have the ability to deal with multiple modes of reasoning. Specifically, the types of reasoning we are concerned with include, among others, reasoning about time, reasoning about quantitative relationships that may be expressed in the form of differential equations or optimization problems, and reasoning about numeric modes of uncertainty about the domain which the database seeks to describe. Such databases may need to handle diverse forms of data structures, and frequently they may require use of the assumption-based nonmonotonic representation of knowledge. A hybrid knowledge base is a theoretical framework capturing all the above modes of reasoning. The theory tightly unifies the constraint logic programming scheme of Jaffar and Lassez (1987), the generalized annotated logic programming theory of Kifer and Subrahmanian (1989), and the stable model semantics of Gelfond and Lifschitz (1988). New techniques are introduced which extend both the work on annotated logic programming and the stable model semantics.

Integrating data and knowledge from multiple heterogeneous sources -- like databases, knowledge bases or specific software packages -- is often required for answering certain queries. Recently, a powerful framework for defining mediated views spanning multiple knowledge bases by a set of constrained rules was proposed [24, 4, 16]. We investigate the materialization of these views by unfolding the view definition and the efficient maintenance of the resulting materialized mediated view in case of updates. Thereby, we consider two kinds of updates: updates to the view and updates to the underlying sources. For each of these two cases several efficient algorithms maintaining materialized mediated views are given. We improve on previous algorithms like the DRed algorithm [12] and introduce a new fixpoint operator WP which -- opposed to the standard fixpoint operator TP [9] -- allows us to correctly capture the update's semantics without any recomputation of the materialized view.