In this innovative approach to the practice of social science, Charles Ragin explores the use of fuzzy sets to bridge the divide between quantitative and qualitative methods. Paradoxically, the fuzzy set is a powerful tool because it replaces an unwieldy, "fuzzy" instrument—the variable, which establishes only the positions of cases relative to each other, with a precise one—degree of membership in a well-defined set. Ragin argues that fuzzy sets allow a far richer dialogue between ideas and evidence in social research than previously possible. They let quantitative researchers abandon "homogenizing assumptions" about cases and causes, they extend diversity-oriented research strategies, and they provide a powerful connection between theory and data analysis. Most important, fuzzy sets can be carefully tailored to fit evolving theoretical concepts, sharpening quantitative tools with in-depth knowledge gained through qualitative, case-oriented inquiry. This book will revolutionize research methods not only in sociology, political science, and anthropology but in any field of inquiry dealing with complex patterns of causation.

Fuzzy-Set Social Science

The SPARQL Query Language for RDF and the SPARQL Protocol for RDF are implemented by a growing number of storage systems and are used within enterprise and open Web settings. As SPARQL is taken up by the community, there is a growing need for benchmarks to compare the performance of storage systems that expose SPARQL endpoints via the SPARQL protocol. Such systems include native RDF stores as well as systems that rewrite SPARQL queries to SQL queries against non-RDF relational databases. This article introduces the Berlin SPARQL Benchmark (BSBM) for comparing the performance of native RDF stores with the performance of SPARQL-to-SQL rewriters across architectures. The benchmark is built around an e-commerce use case in which a set of products is offered by different vendors and consumers have posted reviews about products. The benchmark query mix emulates the search and navigation pattern of a consumer looking for a product. The article discusses the design of the BSBM benchmark and presents the results of a benchmark experiment comparing the performance of four popular RDF stores (Sesame, Virtuoso, Jena TDB, and Jena SDB) with the performance of two SPARQL-to-SQL rewriters (D2R Server and Virtuoso RDF Views) as well as the performance of two relational database management systems (MySQL and Virtuoso RDBMS).

The Berlin SPARQL benchmark

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. The users or service providers with the key have exclusive rights on the data. Especially with popular cloud services, control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. Therefore, this survey focuses on HE and FHE schemes. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars for achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes, are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, and extending the state-of-the-art HE, PHE, SWHE, and FHE systems.

https://dl.acm.org/doi/pdf/10.1145/3214303

A Survey on Homomorphic Encryption Schemes: Theory and Implementation

The generation of RDF data has accelerated to the point where many data sets need to be partitioned across multiple machines in order to achieve reasonable performance when querying the data. Although tremendous progress has been made in the Semantic Web community for achieving high performance data management on a single node, current solutions that allow the data to be partitioned across multiple machines are highly inefficient. In this paper, we introduce a scalable RDF data management system that is up to three orders of magnitude more efficient than popular multi-node RDF data management systems. In so doing, we introduce techniques for (1) leveraging state-of-the-art single node RDF-store technology (2) partitioning the data across nodes in a manner that helps accelerate query processing through locality optimizations and (3) decomposing SPARQL queries into high performance fragments that take advantage of how data is partitioned in a cluster.

/pdf/scalable-sparql-querying-of-large-rdf-graphs-4lsbw1fdh7.pdf

Scalable SPARQL querying of large RDF graphs

Covering attack detection, malware response, algorithm and mechanism design, privacy, and risk management, this comprehensive work applies unique quantitative models derived from decision, control, and game theories to understanding diverse network security problems It provides the reader with a system-level theoretical understanding of network security, and is essential reading for researchers interested in a quantitative approach to key incentive and resource allocation issues in the field It also provides practitioners with an analytical foundation that is useful for formalising decision-making processes in network security

Network Security: A Decision and Game-Theoretic Approach

In this paper we discuss the use of the MapReduce software framework to address the challenge of constructing high-performance, massively-scalable distributed systems. We discuss several design considerations associated with constructing complex distributed systems using the MapReduce software framework, including the difficulty of scalably building indexes. We focus on Hadoop, the most popular MapReduce implementation. Our discussion and analysis are motivated by our construction of SHARD, a massively scalable, high-performance and robust triple-store technology on top of Hadoop. We provide a general approach to construct an information system from the MapReduce software framework that responds to data queries. We provide experimental results generated of an early version of SHARD. We close with a discussion of hypothetical MapReduce alternatives that can be used for the construction of more scalable distributed computing systems.

High-performance, massively scalable distributed systems using the MapReduce software framework: the SHARD triple-store

This paper presents a comparison of performance of various triple-store technologies currently in either production release or beta test. Our comparison of triple-store technologies is biased toward a deployment scenario where the triple-store needs to load data and respond to queries over a very large knowledge base (on the order of hundreds of millions of triples.) The comparisons in this paper are based on the Lehigh University Benchmark (LUBM) software tools. We used the LUBM university ontology, datasets, and standard queries to perform our comparisons. We find that over our test regimen, the triple-stores based on the DAML DB and BigOWLIM technologies exhibit the best performance among the triple-stores tested.

An evaluation of triple-store technologies for large data stores

Graph data processing is an emerging application area for cloud computing because there are few other information infrastructures that cost-effectively permit scalable graph data processing. We present a scalable cloud-based approach to process queries on graph data utilizing the MapReduce model. We call this approach the Clause-Iteration approach. We present algorithms that, when used in conjunction with a MapReduce framework, respond to SPARQL queries over RDF data. Our innovation in the Clause-Iteration approach comes from 1) the iterative construction of query responses by incrementally growing the number of query clauses considered in a response, and 2) our use of flagged keys to join the results of these incremental responses. The Clause-Iteration algorithms form the basis of our scalable, SHARD graph-store built on the Hadoop implementation of MapReduce. SHARD performs favorably when compared to existing "industrial" graph-stores on a standard benchmark graph with 800 million edges. We discuss design considerations and alternatives associated with constructing scalable graph processing technologies.

Clause-iteration with MapReduce to scalably query datagraphs in the SHARD graph-store

This paper discusses problems related to partial observation supervisory controllers with possibly faulty sensors using the framework of discrete-event systems. At initialization all sensors are operational such that the sensors observe occurrences of events and transmit those observations to the controller, but, when a sensor fails, it ceases to send signals to the controller. A new version of observability is introduced that is part of the necessary and sufficient conditions for controller existence under the assumption of faulty sensors. A polynomial-time construction is given that can be used to test for and then synthesize a non-blocking controller with faulty sensors using standard supervisory control methods.

/pdf/sensor-failure-tolerant-supervisory-control-35peu5qpef.pdf

Sensor Failure Tolerant Supervisory Control

In this paper we report on our work to design, implement and evaluate a Fully Homomorphic Encryption (FHE) scheme. Our FHE scheme is an NTRU-like cryptosystem, with additional support for efficient key switching and modulus reduction operations to reduce the frequency of bootstrapping operations. Ciphertexts in our scheme are represented as matrices of 64-bit integers. The basis of our design is a layered software services stack to provide high-level FHE operations supported by lower-level lattice-based primitive implementations running on a computing substrate. We implement and evaluate our FHE scheme to run on a commodity CPU-based computing environment. We implemented our FHE scheme to run in a compiled C environment and use parallelism to take advantage of multi-core processors. We provide experimental results which show that our FHE implementation provides at least an order of magnitude improvement in runtime as compared to recent publicly known evaluation results of other FHE software implementations.

Kurt Rohloff

Papers

High-performance, massively scalable distributed systems using the MapReduce software framework: the SHARD triple-store

An evaluation of triple-store technologies for large data stores

Clause-iteration with MapReduce to scalably query datagraphs in the SHARD graph-store

Sensor Failure Tolerant Supervisory Control

A scalable implementation of fully homomorphic encryption built on NTRU