SmartClean: an incremental data cleaning tool

This paper presents the SmartClean tool. The purpose of this tool is to detect and correct the data quality problems (DQPs). Compared with existing tools, SmartClean has the following main advantage: the user does not need to specify the execution sequence of the data cleaning operations. For that, an execution sequence was developed. The problems are manipulated (i.e., detected and corrected) following that sequence. The sequence also supports the incremental execution of the operations. In this paper, the underlying architecture of the tool is presented and its components are described in detail. The tool's validity and, consequently, of the architecture is demonstrated through the presentation of a case study. Although SmartClean has cleaning capabilities in all other levels, in this paper are only described those related with the attribute value level.

Ontologies for reusing data cleaning knowledge

The emergence of new business models, namely, the establishment of partnerships between organizations, the chance that companies have of adding existing data on the web, especially in the semantic web, to their information, led to the emphasis on some problems existing in databases, particularly related to data quality. Poor data can result in loss of competitiveness of the organizations holding these data, and may even lead to their disappearance, since many of their decision-making processes are based on these data. For this reason, data cleaning is essential. Current approaches to solve these problems are closely linked to database schemas and specific domains. In order that data cleaning can be used in different repositories, it is necessary for computer systems to understand these data, i.e., an associated semantic is needed. The solution presented in this paper includes the use of ontologies: (i) for the specification of data cleaning operations and, (ii) as a way of solving the semantic heterogeneity problems of data stored in different sources. With data cleaning operations defined at a conceptual level and existing mappings between domain ontologies and an ontology that results from a database, they may be instantiated and proposed to the expert/specialist to be executed over that database, thus enabling their interoperability.

FAMOUS, faster: using parallel computing techniques to accelerate the FAMOUS/HadCM3 climate model with a focus on the radiative transfer algorithm

We have optimised the atmospheric radiation algorithm of the FAMOUS climate model on several hardware platforms. The optimisation involved translating the Fortran code to C and restructuring the algorithm around the computation of a single air column. Instead of the existing MPI-based domain decomposition, we used a task queue and a thread pool to schedule the computation of individual columns on the available processors. Finally, four air columns are packed together in a single data structure and computed simultaneously using Single Instruction Multiple Data operations. The modified algorithm runs more than 50 times faster on the CELL’s Synergistic Processing Elements than on its main PowerPC processing element. On Intel-compatible processors, the new radiation code runs 4 times faster. On the tested graphics processor, using OpenCL, we find a speed-up of more than 2.5 times as compared to the original code on the main CPU. Because the radiation code takes more than 60% of the total CPU time, FAMOUS executes more than twice as fast. Our version of the algorithm returns bit-wise identical results, which demonstrates the robustness of our approach. We estimate that this project required around two and a half man-years of work.

An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.

Elementary Petri net inside RFID distributed database (PNRD)

Usually, a Petri net is applied as an RFID model tool. This paper, otherwise, presents another approach to the Petri net concerning RFID systems. This approach, called elementary Petri net inside an RFID distributed database, or PNRD, is the first step to improve RFID and control systems integration, based on a formal data structure to identify and update the product state in real-time process execution, allowing automatic discovery of unexpected events during tag data capture. There are two main features in this approach: to use RFID tags as the object process expected database and last product state identification; and to apply Petri net analysis to automatically update the last product state registry during reader data capture. RFID reader data capture can be viewed, in Petri nets, as a direct analysis of locality for a specific transition that holds in a specific workflow. Following this direction, RFID readers storage Petri net control vector list related to each tag id is expected to be perceived. This paper presents PNRD cornerstones and a PNRD implementation example in software called DEMIS Distributed Environment in Manufacturing Information Systems.

An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

Memory hierarchy and data communication in heterogeneous reconfigurable SoCs

The miniaturization race in the hardware industry aiming at continuous increasing of transistor density on a die does not bring respective application performance improvements any more. One of the most promising alternatives is to exploit a heterogeneous nature of common applications in hardware. Supported by reconfigurable computation, which has already proved its efficiency in accelerating data intensive applications, this concept promises a breakthrough in contemporary technology development. Memory organization in such heterogeneous reconfigurable architectures becomes very critical. Two primary aspects introduce a sophisticated trade-off. On the one hand, a memory subsystem should provide well organized distributed data structure and guarantee the required data bandwidth. On the other hand, it should hide the heterogeneous hardware structure from the end-user, in order to support feasible high-level programmability of the system. This thesis work explores the heterogeneous reconfigurable hardware architectures and presents possible solutions to cope the problem of memory organization and data structure. By the example of the MORPHEUS heterogeneous platform, the discussion follows the complete design cycle, starting from decision making and justification, until hardware realization. Particular emphasis is made on the methods to support high system performance, meet application requirements, and provide a user-friendly programmer interface. As a result, the research introduces a complete heterogeneous platform enhanced with a hierarchical memory organization, which copes with its task by means of separating computation from communication, providing reconfigurable engines with computation and configuration data, and unification of heterogeneous computational devices using local storage buffers. It is distinguished from the related solutions by distributed data-flow organization, specifically engineered mechanisms to operate with data on local domains, particular communication infrastructure based on Network-on-Chip, and thorough methods to prevent computation and communication stalls. In addition, a novel advanced technique to accelerate memory access was developed and implemented.

Hardware/Software Design of Dynamic Real-Time Schedulers for Embedded Multiprocessor Systems

The new generation of multicore processors opens new perspectives for the design of embedded systems. Multiprocessing, however, poses new challenges to the scheduling of real-time applications, in which the ever-increasing computational demands are constantly flanked by the need of meeting critical time constraints. Many research works have contributed to this field introducing new advanced scheduling algorithms. However, despite many of these works have solidly demonstrated their effectiveness, the actual support for multiprocessor real-time scheduling offered by current operating systems is still very limited. This dissertation deals with implementative aspects of real-time schedulers in modern embedded multiprocessor systems. The first contribution is represented by an open-source scheduling framework, which is capable of realizing complex multiprocessor scheduling policies, such as G-EDF, on conventional operating systems exploiting only their native scheduler from user-space. A set of experimental evaluations compare the proposed solution to other research projects that pursue the same goals by means of kernel modifications, highlighting comparable scheduling performances. The principles that underpin the operation of the framework, originally designed for symmetric multiprocessors, have been further extended first to asymmetric ones, which are subjected to major restrictions such as the lack of support for task migrations, and later to re-programmable hardware architectures (FPGAs). In the latter case, this work introduces a scheduling accelerator, which offloads most of the scheduling operations to the hardware and exhibits extremely low scheduling jitter. The realization of a portable scheduling framework presented many interesting software challenges. One of these has been represented by timekeeping. In this regard, a further contribution is represented by a novel data structure, called addressable binary heap (ABH). Such ABH, which is conceptually a pointer-based implementation of a binary heap, shows very interesting average and worst-case performances when addressing the problem of tick-less timekeeping of high-resolution timers.

Advanced data deduplication techniques and their application

Data deduplication describes a class of approaches that reduce the storage capacity needed to store data or the amount of data that has to be transferred over a network. These approaches detect coarse-grained redundancies within a data set, e.g. a file system, and remove them.rnrnOne of the most important applications of data deduplication are backup storage systems where these approaches are able to reduce the storage requirements to a small fraction of the logical backup data size.rnThis thesis introduces multiple new extensions of so-called fingerprinting-based data deduplication. It starts with the presentation of a novel system design, which allows using a cluster of servers to perform exact data deduplication with small chunks in a scalable way.rnrnAfterwards, a combination of compression approaches for an important, but often over- looked, data structure in data deduplication systems, so called block and file recipes, is introduced. Using these compression approaches that exploit unique properties of data deduplication systems, the size of these recipes can be reduced by more than 92% in all investigated data sets. As file recipes can occupy a significant fraction of the overall storage capacity of data deduplication systems, the compression enables significant savings.rnrnA technique to increase the write throughput of data deduplication systems, based on the aforementioned block and file recipes, is introduced next. The novel Block Locality Caching (BLC) uses properties of block and file recipes to overcome the chunk lookup disk bottleneck of data deduplication systems. This chunk lookup disk bottleneck either limits the scalability or the throughput of data deduplication systems. The presented BLC overcomes the disk bottleneck more efficiently than existing approaches. Furthermore, it is shown that it is less prone to aging effects.rnrnFinally, it is investigated if large HPC storage systems inhibit redundancies that can be found by fingerprinting-based data deduplication. Over 3 PB of HPC storage data from different data sets have been analyzed. In most data sets, between 20 and 30% of the data can be classified as redundant. According to these results, future work in HPC storage systems should further investigate how data deduplication can be integrated into future HPC storage systems.rnrnThis thesis presents important novel work in different area of data deduplication re- search.

Dynamic distance analysis : new geometric data structures and algorithms for the real-time calculation of tolerance violating regions in the digital mockup process

In vielen Industriezweigen, zum Beispiel in der Automobilindustrie, werden Digitale Versuchsmodelle (Digital MockUps) eingesetzt, um die Konstruktion und die Funktion eines Produkts am virtuellen Prototypen zu überprüfen. Ein Anwendungsfall ist dabei die Überprüfung von Sicherheitsabständen einzelner Bauteile, die sogenannte Abstandsanalyse. Ingenieure ermitteln dabei für bestimmte Bauteile, ob diese in ihrer Ruhelage sowie während einer Bewegung einen vorgegeben Sicherheitsabstand zu den umgebenden Bauteilen einhalten. Unterschreiten Bauteile den Sicherheitsabstand, so muss deren Form oder Lage verändert werden. Dazu ist es wichtig, die Bereiche der Bauteile, welche den Sicherhabstand verletzen, genau zu kennen. rnrnIn dieser Arbeit präsentieren wir eine Lösung zur Echtzeitberechnung aller den Sicherheitsabstand unterschreitenden Bereiche zwischen zwei geometrischen Objekten. Die Objekte sind dabei jeweils als Menge von Primitiven (z.B. Dreiecken) gegeben. Für jeden Zeitpunkt, in dem eine Transformation auf eines der Objekte angewendet wird, berechnen wir die Menge aller den Sicherheitsabstand unterschreitenden Primitive und bezeichnen diese als die Menge aller toleranzverletzenden Primitive. Wir präsentieren in dieser Arbeit eine ganzheitliche Lösung, welche sich in die folgenden drei großen Themengebiete unterteilen lässt.rnrnIm ersten Teil dieser Arbeit untersuchen wir Algorithmen, die für zwei Dreiecke überprüfen, ob diese toleranzverletzend sind. Hierfür präsentieren wir verschiedene Ansätze für Dreiecks-Dreiecks Toleranztests und zeigen, dass spezielle Toleranztests deutlich performanter sind als bisher verwendete Abstandsberechnungen. Im Fokus unserer Arbeit steht dabei die Entwicklung eines neuartigen Toleranztests, welcher im Dualraum arbeitet. In all unseren Benchmarks zur Berechnung aller toleranzverletzenden Primitive beweist sich unser Ansatz im dualen Raum immer als der Performanteste.rnrnDer zweite Teil dieser Arbeit befasst sich mit Datenstrukturen und Algorithmen zur Echtzeitberechnung aller toleranzverletzenden Primitive zwischen zwei geometrischen Objekten. Wir entwickeln eine kombinierte Datenstruktur, die sich aus einer flachen hierarchischen Datenstruktur und mehreren Uniform Grids zusammensetzt. Um effiziente Laufzeiten zu gewährleisten ist es vor allem wichtig, den geforderten Sicherheitsabstand sinnvoll im Design der Datenstrukturen und der Anfragealgorithmen zu beachten. Wir präsentieren hierzu Lösungen, die die Menge der zu testenden Paare von Primitiven schnell bestimmen. Darüber hinaus entwickeln wir Strategien, wie Primitive als toleranzverletzend erkannt werden können, ohne einen aufwändigen Primitiv-Primitiv Toleranztest zu berechnen. In unseren Benchmarks zeigen wir, dass wir mit unseren Lösungen in der Lage sind, in Echtzeit alle toleranzverletzenden Primitive zwischen zwei komplexen geometrischen Objekten, bestehend aus jeweils vielen hunderttausend Primitiven, zu berechnen. rnrnIm dritten Teil präsentieren wir eine neuartige, speicheroptimierte Datenstruktur zur Verwaltung der Zellinhalte der zuvor verwendeten Uniform Grids. Wir bezeichnen diese Datenstruktur als Shrubs. Bisherige Ansätze zur Speicheroptimierung von Uniform Grids beziehen sich vor allem auf Hashing Methoden. Diese reduzieren aber nicht den Speicherverbrauch der Zellinhalte. In unserem Anwendungsfall haben benachbarte Zellen oft ähnliche Inhalte. Unser Ansatz ist in der Lage, den Speicherbedarf der Zellinhalte eines Uniform Grids, basierend auf den redundanten Zellinhalten, verlustlos auf ein fünftel der bisherigen Größe zu komprimieren und zur Laufzeit zu dekomprimieren.rnrnAbschießend zeigen wir, wie unsere Lösung zur Berechnung aller toleranzverletzenden Primitive Anwendung in der Praxis finden kann. Neben der reinen Abstandsanalyse zeigen wir Anwendungen für verschiedene Problemstellungen der Pfadplanung.

Estimation with Interval Censored Data in Longitudinal Studies

In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event variable T. If one observes whether or not T exceeds an observed monitoring time at a random number of monitoring times, then the data structure is called interval censored data. We extend this data structure by allowing the presence of a possibly time-dependent covariate process that is observed until end of follow up. If one only assumes that the censoring mechanism satisfies coarsening at random, then, by the curve of dimensionality, typically no regular estimators will exist. To fight the curse of dimensionality we follow the approach of Robins and Rotnitzky (1992) by modeling parameters of the censoring mechanism. We model the right-censoring mechanism by modeling the hazard of the follow up time, conditional on T and the covariate process. For the monitoring mechanism we avoid modeling the joint distribution of the monitoring times by only modeling a univariate hazard of the pooled monitoring times, conditional on the follow up time, T, and the covariates process, which can be estimated by treating the pooled sample of monitoring times as i.i.d. In particular, it is assumed that the monitoring times and the right-censoring times only depend on T through the observed covariate process. We introduce inverse probability of censoring weighted (IPCW) estimator of the distribution of T and of smooth functionals thereof which are guaranteed to be consistent and asymptotically normal if we have available correctly specified semiparametric models for the two hazards of the censoring process. Furthermore, given such correctly specified models for these hazards of the censoring process, we propose a one-step estimator which will improve on the IPCW estimator if we correctly specify a lower-dimensional working model for the conditional distribution of T, given the covariate process, that remains consistent and asymptotically normal if this latter working model is misspecified. It is shown that the one-step estimator is efficient if each subject is at most monitored once and the working model contains the truth. In general, it is shown that the one-step estimator optimally uses the surrogate information if the working model contains the truth. It is not optimal in using the interval information provided by the current status indicators at the monitoring times, but simulations in Peterson, van der Laan (1997) show that the efficiency loss is small.

Locally Efficient Estimation in Censored Data Models: Theory and Examples

In many applications the observed data can be viewed as a censored high dimensional full data random variable X. By the curve of dimensionality it is typically not possible to construct estimators that are asymptotically efficient at every probability distribution in a semiparametric censored data model of such a high dimensional censored data structure. We provide a general method for construction of one-step estimators that are efficient at a chosen submodel of the full-data model, are still well behaved off this submodel and can be chosen to always improve on a given initial estimator. These one-step estimators rely on good estimators of the censoring mechanism and thus will require a parametric or semiparametric model for the censoring mechanism. We present a general theorem that provides a template for proving the desired asymptotic results. We illustrate the general one-step estimation methods by constructing locally efficient one-step estimators of marginal distributions and regression parameters with right-censored data, current status data and bivariate right-censored data, in all models allowing the presence of time-dependent covariates. The conditions of the asymptotics theorem are rigorously verified in one of the examples and the key condition of the general theorem is verified for all examples.

Generalizations of Current Status Data With Applications

In estimation of a survival function, current status data arises when the only information available on individuals is their survival status at a single monitoring time. Here we briefly review extensions of this form of data structure in two directions: (i) doubly censored current status data, where there is incomplete information on the origin of the failure time random variable, and (ii) current status information on more complicated stochastic processes. Simple examples of these data forms are presented for motivation.

Privacy Preserving Probabilistic Record Linkage (P3RL): a novel method for linking existing health-related data and maintaining participant confidentiality.

BACKGROUND Record linkage of existing individual health care data is an efficient way to answer important epidemiological research questions. Reuse of individual health-related data faces several problems: Either a unique personal identifier, like social security number, is not available or non-unique person identifiable information, like names, are privacy protected and cannot be accessed. A solution to protect privacy in probabilistic record linkages is to encrypt these sensitive information. Unfortunately, encrypted hash codes of two names differ completely if the plain names differ only by a single character. Therefore, standard encryption methods cannot be applied. To overcome these challenges, we developed the Privacy Preserving Probabilistic Record Linkage (P3RL) method. METHODS In this Privacy Preserving Probabilistic Record Linkage method we apply a three-party protocol, with two sites collecting individual data and an independent trusted linkage center as the third partner. Our method consists of three main steps: pre-processing, encryption and probabilistic record linkage. Data pre-processing and encryption are done at the sites by local personnel. To guarantee similar quality and format of variables and identical encryption procedure at each site, the linkage center generates semi-automated pre-processing and encryption templates. To retrieve information (i.e. data structure) for the creation of templates without ever accessing plain person identifiable information, we introduced a novel method of data masking. Sensitive string variables are encrypted using Bloom filters, which enables calculation of similarity coefficients. For date variables, we developed special encryption procedures to handle the most common date errors. The linkage center performs probabilistic record linkage with encrypted person identifiable information and plain non-sensitive variables. RESULTS In this paper we describe step by step how to link existing health-related data using encryption methods to preserve privacy of persons in the study. CONCLUSION Privacy Preserving Probabilistic Record linkage expands record linkage facilities in settings where a unique identifier is unavailable and/or regulations restrict access to the non-unique person identifiable information needed to link existing health-related data sets. Automated pre-processing and encryption fully protect sensitive information ensuring participant confidentiality. This method is suitable not just for epidemiological research but also for any setting with similar challenges.

Fast Algorithms Using Minimal Data Structures for Common Topological Relationships in Large, Irregularly-spaced Topographic Data Sets

Digital terrain models (DTM) typically contain large numbers of postings, from hundreds of thousands to billions. Many algorithms that run on DTMs require topological knowledge of the postings, such as finding nearest neighbors, finding the posting closest to a chosen location, etc. If the postings are arranged irregu- larly, topological information is costly to compute and to store. This paper offers a practical approach to organizing and searching irregularly-space data sets by presenting a collection of efficient algorithms (O(N),O(lgN)) that compute important topological relationships with only a simple supporting data structure. These relationships include finding the postings within a window, locating the posting nearest a point of interest, finding the neighborhood of postings nearest a point of interest, and ordering the neighborhood counter-clockwise. These algorithms depend only on two sorted arrays of two-element tuples, holding a planimetric coordinate and an integer identification number indicating which posting the coordinate belongs to. There is one array for each planimetric coordinate (eastings and northings). These two arrays cost minimal overhead to create and store but permit the data to remain arranged irregularly.