Decidability of linearizabilities for relaxed data structures

Many recent implementations of concurrent data structures relaxed their linearizability requirements for better performance and scalability. Quasi-linearizability, k-linearizability and regular-relaxed linearizability are three quantitative relaxation variants of linearizability that have been proposed as correctness conditions of relaxed data structures, yet preserving the intuition of linearizability. Quasi-linearizability has been proved undecidable. In this paper, we first show that k-linearizability is undecidable for a bounded number of processes, by reducing quasi-linearizability into it. We then show that regular-relaxed linearizability is decidable for a bounded number of processes. We also find that the number of the states of a relaxed specification is exponential to the number of the states of the underlying specification automaton (representing its relaxation strategy), and polynomial to the number of the states of the underlying quantitative sequential specification and the number of operations.     

On implicit data structures for priority queues

Certain implicit data structures for priority queues are implemented and compared with a simple linked structure. Timing experiments show that implicit structures are rather slow.     

Stochastic regulation of queues in data networks

A new class of routing policies for the regulation of queue lengths at all nodes throughout a data network in the presence of congestion caused by heavy input traffic is developed. Regulation is achieved via a feedback algorithm which minimizes, over each successive control interval, the mean-square queueing error where the latter is the difference between the actual queueing vector and its desired nominal value. Implementation of the algorithm requires knowledge of the mean and covariance of the input traffic and of the queue length measurement error stochastic processes. In the case of the former process, on-line measurements can be used to calculate the required statistical parameters. For the latter process, a priori knowledge that reflects the uncertainty in measuring and transmitting queue lengths throughout the network is needed.     

Functional data structures as updatable objects

The threat of continuous data copying is one of the key problems in applicative programming. The opportunities to implement functional data structures as updatable objects, thus avoiding any form of copying, are discussed. The notion of incremental structures and associated storage is used. Its specification is modified so as to handle sequential and shared structures. The former can be used in the case when only data dependencies and not data values are considered in source program analysis. The latter can be used for nondeterministic applications such as system programming     

Clustering high dimensional data: A graph-based relaxed optimization approach

There is no doubt that clustering is one of the most studied data mining tasks. Nevertheless, it remains a challenging problem to solve despite the many proposed clustering approaches. Graph-based approaches solve the clustering task as a global optimization problem, while many other works are based on local methods. In this paper, we propose a novel graph-based algorithm “GBR” that relaxes some well-defined method even as improving the accuracy whilst keeping it simple. The primary motivation of our relaxation of the objective is to allow the reformulated objective to find well distributed cluster indicators for complicated data instances. This relaxation results in an analytical solution that avoids the approximated iterative methods that have been adopted in many other graph-based approaches. The experiments on synthetic and real data sets show that our relaxation accomplishes excellent clustering results. Our key contributions are: (1) we provide an analytical solution to solve the global clustering task as opposed to approximated iterative approaches; (2) a very simple implementation using existing optimization packages; (3) an algorithm with relatively less computation time over the number of data instances to cluster than other well defined methods in the literature.     

Crime data mining: a general framework and some examples

A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting cybercrime can likewise be difficult because busy network traffic and frequent online transactions generate large amounts of data, only a small portion of which relates to illegal activities. Data mining is a powerful tool that enables criminal investigators who may lack extensive training as data analysts to explore large databases quickly and efficiently. We present a general framework for crime data mining that draws on experience gained with the Coplink project, which researchers at the University of Arizona have been conducting in collaboration with the Tucson and Phoenix police departments since 1997.     

The verification and synthesis of data structures

Summary The concept of machine extension is a commonly used technique for implementing complex software: sets of object classes and operations on these objects are defined and used, often in a layered fashion, to construct the system. This paper addresses the adaptation of this technique to automatic programming. It discusses how such sets of data structures may be precisely specified, presents an axiomatization of a programming language suitable for machine verification, and shows how programs which realize these data structures may be proved correct. A range of data type classes is treated—including arrays, records, and pointers. Some new verification rules are presented to handle programs which use assignments and structured objects.     

The Advanced Video Information System: data structures and query processing

We describe how video data can be organized and structured so as to facilitate efficient querying. We develop a formal model for video data and show how spatial data structures, suitably modified, provide an elegant way of storing such data. We develop algorithms to process various kinds of video queries and show that, in most cases, the complexity of these algorithms is linear. A prototype system, called the Advanced Video Information System (AVIS), based on these concepts, has been designed at the University of Maryland.     

The expressive theory of stacks

Summary The usual theory of stacks is not expressive in the sense of Cook; that is, loop invariants needed to prove programs that use stacks cannot be stated in the logic. We first prove this assertion, then suggest ways of augmenting theories with new operators so as to achieve expressiveness. The main technique is to regard data types as function spaces. The technique is applied to stacks as well as to other data types.     

Multilevel data structures: models and performance

A stepwise method of deriving the high-performance implementation of a set of operations is proposed. This method is based on the ability to organize the data into a multilevel data structure to provide an efficient implementation of all the operations. Typically, for such data organization the performance may deteriorate over a period of time and that can be corrected by reorganizing the data. This data reorganization is done by the introduction of maintenance processes. For a particular example, the multilevel data organization and the different models of maintenance processes possible are considered. The various models of maintenance process provide varying amounts of concurrency by varying the degree of atomicity in different operations. Performance behavior for the different models is derived and a correctness proof for the developed implementation is outlined     

Evaluation of integrated data sets—Four examples

Several large data sets have been integrated and utilized for rapid evaluation on a reconnaissance scale for the Montrose 1 × 2° Quadrangle, Colorado. The data sets include Landsat imagery, hydrogeochemical and stream sediment analyses, airborne geophysical data, known mineral occurrences, and a geologic map. All data sets were registered to a 179 × 119 rectangular grid and projected onto Universal Transverse Mercator coordinates. A grid resolution of 1 km was used. All possible combinations of three, for most data sets, were examined for general geologic correlations by utilizing a color microfilm output. In addition, gray-level pictures of statistical output, for example factor analysis, have been employed to aid evaluations. Examples for the data sets dysprosium-calcium, lead-copper-zinc, and equivalent uranium-uranium in water-uranium in sediment are described with respect to geologic applications, base-metal regimes, and geochemical associations.     

The measurement of infrastructure capacity: Theory, data structures, and analytics

Recent trends in the adoption of growth management policies emphasize the use of infrastructure as the means for influencing land development, thereby reawakening discussion of the theory and method of measurement of infrastructure capacity. This paper explores the conceptual and empirical dimensions of capacity measurement in the six most capital-intensive infrastructure systems: schools, roads, sewers, water, storm drainage, and solid waste disposal.     

Compressed data structures: Dictionaries and data-aware measures

In this paper, we propose measures for compressed data structures, in which space usage is measured in a data-aware manner. In particular, we consider the fundamental dictionary problem on set data  , where the task is to construct a data structure for representing a set S$S$ of n$n$ items out of a universe U={0,…,u−1}$U=\{0,\dots ,u-1\}$ and supporting various queries on S$S$. We use a well-known data-aware measure for set data called gap to bound the space of our data structures.     

Some examples of management and processing of geological and geochemical data

Three geochemical and geological data bases are described using the same data-file management system. The paper first describes the data-base system, secondly indicates the nature and volume of the three data bases, and lastly gives some examples of data retrieval and processing.     

Building pattern recognition in topographic data: examples on collinear and curvilinear alignments

Building patterns are important features that should be preserved in the map generalization process. However, the patterns are not explicitly accessible to automated systems. This paper proposes a framework and several algorithms that automatically recognize building patterns from topographic data, with a focus on collinear and curvilinear alignments. For both patterns two algorithms are developed, which are able to recognize alignment-of-center and alignment-of-side patterns. The presented approach integrates aspects of computational geometry, graph-theoretic concepts and theories of visual perception. Although the individual algorithms for collinear and curvilinear patterns show great potential for each type of the patterns, the recognized patterns are neither complete nor of enough good quality. We therefore advocate the use of a multi-algorithm paradigm, where a mechanism is proposed to combine results from different algorithms to improve the recognition quality. The potential of our method is demonstrated by an application of the framework to several real topographic datasets. The quality of the recognition results are validated in an expert survey.     

The cell probe complexity of succinct data structures

We consider time-space tradeoffs for static data structure problems in the cell probe model with word size 1 (the bit probe model). In this model, the goal is to represent n$n$-bit data with s=n+r$s=n+r$ bits such that queries (of a certain type) about the data can be answered by reading at most t$t$ bits of the representation. Ideally, we would like to keep both s$s$ and t$t$ small, but there are tradeoffs between the values of s$s$ and t$t$ that limit the possibilities of keeping both parameters small.     

Data quality,sensitive data and joint controllership as examples of grey areas in the existing data protection framework for the Internet of Things

‘The technology used to provide connectivity from anytime, any place and for anyone. Now anything can be added to the list.’¹¹ITU Internet reports 2005: ‘The Internet of Things’ <http://www.itu.int/dms_pub/itus/opb/pol/S-POL-IR.IT-2005-SUM-PDF-E.pdf> 2, accessed 27 April 2015.View all notes In the Internet of Things (‘IoT’), the amount of individuals’ data collected and processed is increasing substantially as data are being collected from various sources. Most communications between smart devices occur automatically, potentially without the user being aware of it. Many questions arise around the vulnerability of the devices in the IoT, often deployed outside a traditional IT structure and lacking sufficient built-in security. The IoT demands consideration and research into how to best balance the opportunities that the IoT affords against legal risks it imposes on data protection. Considerable questions about how our currently existing EU framework for protection of personal data applies in IoT are being raised. The data protection legislation needs to move from theory to practice and in order to achieve this; the legal framework may need additional mechanisms.