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Today's low-power devices, such as smartphones and wearables, form a very heterogeneous ecosystem. Applications in such a system typically follow a reactive pattern based on stream analytics, i.e., sensing, processing, and actuating. Despite the simplicity of this pattern, deciding where to place the processing tasks of an application to achieve energy efficiency is non-trivial in a heterogeneous system since application components are distributed across multiple devices. In this paper, we present Aves - a decision-making engine based on a holistic energy-prediction model, with which the processing tasks of applications can be placed automatically in an energy-efficient manner without programmer/user intervention. We validate the effectiveness of the model and reveal several counter-intuitive placement decisions. Our decision engine's improvements are typically 10-30%, with up to a factor 14 in the most extreme cases. We also show that Aves gives an accurate decision in comparison with real energy measurements for two sensor-based applications.

Complex Event Processing (CEP) is a powerful paradigm for scalable data management that is employed in many real-world scenarios such as detecting credit card fraud in banks. The so-called complex events are expressed using a specification language that is typically implemented and executed on a specific runtime system. While the tight coupling of these two components has been regarded as the key for supporting CEP at high performance, such dependencies pose several inherent challenges as follows. (1) Application development atop a CEP system requires extensive knowledge of how the runtime system operates, which is typically highly complex in nature. (2) The specification language dependence requires the need of domain experts and further restricts and steepens the learning curve for application developers. In this paper, we propose CEPLESS, a scalable data management system that decouples the specification from the runtime system by building on the principles of serverless computing. CEPLESS provides operator as a service and offers flexibility by enabling the development of CEP application in any specification language while abstracting away the complexity of the CEP runtime system. As part of CEPLESS, we designed and evaluated novel mechanisms for in-memory processing and batching that enables the stateful processing of CEP operators even under high rates of ingested events. Our evaluation demonstrates that CEPLESS can be easily integrated into existing CEP systems like Apache Flink while attaining similar throughput under a high scale of events (up to 100K events per second) and dynamic operator update in up to 238 ms., 10 pages, Published in the Proceedings of the IEEE International Conference on Big Data

Sensor-based applications using Big Data are of increasing importance in various fields. A typical example of such use cases is building health-care applications [1], [2]. A typical scenario is where a patient's heart rate is monitored by a smartwatch. A smartphone can then analyze the gathered data and identify patterns in the patient's heart rate. However, if the data analysis is too complex to be performed on a smartphone, the computation could be offloaded to a nearby cloudlet or a remote cloud. A decision usually follows the analysis, and actuation is performed accordingly (e.g., a message is sent to either the patient or the doctor). Developing such an application is intrinsically complex, as the programmer needs to reconcile different APIs specific to different platforms.

The COVID-19 pandemic has generated an overall slowdown in hospital activities that might lead to delays in healthcare interventions, and the scarcity of resources can raise concerns about ventilators allocation criteria. These circumstances could lead to lawsuits against hospitals and healthcare professionals: together with Regions and States, they may be vulnerable to legal actions, due to the breach of right to health, to physical integrity and right to life, to the manifestation of the informed consent in the medical field or on the basis of contractual or Aquilian obligations. In this context, predicting the litigation rate could be useful to assess the economic impact of a dispute at a local and national level, so that hospital managers and public institutions can perform multi-dimensional and cost/benefit evaluations to decide whether to invest resources to increase critical care surge capacity. In this work we present CLIP (COVID-19 LItigation Prediction), a modeling approach supported by swarm intelligence designed to forecast the occupancy of intensive care units using COVID-19 time-series. CLIP fits a logistic model of COVID-19 patients admission in order to estimate the future number of patients, and then exploits a probabilistic model to predict the number of occupied intensive care beds, whose parameters are calibrated by means of Fuzzy Self-Tuning Particle Swarm Optimization. We assume that each individual rejected from an intensive care unit due to the lack of resources should be considered a potential plaintiff. The development and the availability of such a predictive model, that could further be used within other clinical conditions and important diseases, could help policy-makers in taking decisions under conditions of uncertainty.

Because of the different genetic structures and functional gene diversity, bacterial genome data have high complexity and dimensions. Therefore, it is difficult to reveal the sequence patterns and biological mechanism of a genome with classical analysis methods. Since artificial intelligence (AI) applications are capable of mining key biological information from massive multidimensional data, they are broadly employed to analyze bacterial genomes. However, to our knowledge, there are few systematic reviews that illustrate these AI applications in bacterial genomics research. Therefore, we first introduce the characteristics of bacterial genomics, and then briefly summarize AI applications in bacterial genomics research from three aspects: gene finding, gene function prediction and gene expression network construction. Finally, we discuss the challenges and future AI applications in bacterial genomics research.

MapReduce ecosystems are (still) widely popular for big data processing in data centers. To address the diverse non-functional requirements arising from many and increasingly more sophisticated users, the community has developed many scheduling policies for MapReduce workloads. Although some individual policies can dynamically optimize for single and stable performance objectives, such as minimizing runtime or cost, or meeting deadlines for realtime-jobs, it seems unlikely that individual policies will remain competitive for increasingly more dynamic workloads and objectives. In contrast, in this work we investigate the ability to dynamically balance performance and cost of a portfolio scheduler for MapReduce workloads. To this end, we design and implement a portfolio scheduling technique, that is, a system capable of adapting to the current workload characteristics and target objectives by periodically evaluating its set of potential policies, and of switching to »the best» policy that targets the current system state. We implement and evaluate our system with real-world experiments on a workload containing a mixture of real-time and batch jobs, with the purpose of minimizing deadline violations, while keeping batch job slowdown in check. Our results show that POSUM is a promising alternative: it can out-perform the individual policies of its portfolio for the combined optimization goal, even without precise predictions.

A very large number of online cultural heritage (CH) resources is made available through numerous digital libraries. To address the difficulties of discoverability in CH, the common practice is metadata aggregation, where centralized efforts like Europeana facilitate discoverability by collecting the resources' metadata. In the last years, the CH domain has invested in data models for Linked Data (LD) representation of CH metadata. LD, however, also has potential for innovating metadata aggregation. We present the results of a pilot case study within the Europeana Network. In this pilot, the National Library of The Netherlands plays the role of initial data provider, with the Dutch Digital Heritage Network the one of intermediary service providing datasets to Europeana. We analysed the requirements for an LD aggregation solution and defined a workflow that fulfils the same functional requirements as Europeana's current solution. The workflow was put into practice within the pilot and led to the development of several software components for managing datasets, harvesting LD, data analysis and integration. Our analysis of the experience discusses the effort of adopting such an LD approach for data providers and aggregators, the expertise required by CH data analysts, and the supporting tools required for semantic data.

Active learning is a technique that helps to minimize the annotation budget required for the creation of a labeled dataset while maximizing the performance of a model trained on this dataset. It has been shown that active learning can be successfully applied to sequence tagging tasks of text processing in conjunction with deep learning models even when a limited amount of labeled data is available. Recent advances in transfer learning methods for natural language processing based on deep pre-trained models such as ELMo and BERT offer a much better ability to generalize on small annotated datasets compared to their shallow counterparts. The combination of deep pre-trained models and active learning leads to a powerful approach to dealing with annotation scarcity. In this work, we investigate the potential of this approach on clinical and biomedical data. The experimental evaluation shows that the combination of active learning and deep pre-trained models outperforms the standard methods of active learning. We also suggest a modification to a standard uncertainty sampling strategy and empirically show that it could be beneficial for annotation of very skewed datasets. Finally, we propose an annotation tool empowered with active learning and deep pre-trained models that could be used for entity annotation directly from Jupyter IDE.

GDPR (General Data Protection Regulation) is a new regulation of the European Union that superimposes strict privacy constraints on storing, accessing and processing user data, as a way to ensure that personal user data are not violated neither disclosed without an explicit consent. As a consequence, business processes that interact with large amounts of such data may easily cause GDPR violations, due to the typical complexity of such processes. Inspired by these considerations, this paper highlights the challenges and critical aspects associated with the GDPR compliance journey when opting for naïve straight-forward solutions. We propose a business-aware GDPR compliance journey using online process mining. Using several large log files generated based on a real scenario, we show that the proposed tool is both effective and efficient. As such, it proves to be a powerful concept for usage in incremental GDPR compliance environments.

To help scholars to extract meaning, knowledge and value from large volumes of archival content, such as the Dutch Common Lab Research Infrastructure for the Arts and Humanities (CLARIAH), we need to provide more ‘generous’ access to the data than can be provided with generalised search and visualisation tools alone. Our approach is to use Jupyter Notebooks in combination with the existing archive APIs (Application Programming Interface). This gives access to both the archive metadata and a wide range of analysis and visualisation techniques. We have created notebooks and modules of supporting functions that enable the overview, investigation and analysis of the archive. We demonstrate the value of our approach in preliminary tests of its use in scholarly research, and give our observations of the potential value for archivists. Finally, we show that good archive knowledge is essential to create correct and meaningful visualisations and statistics.

Computerized safety checklist systems designed for improving care-givers' situation awareness are emerging in recent years. These systems are proved to be effective for improving adherence to guidelines while keeping high user acceptance. However, most of these systems are still hard-coded since it is yet unclear to what extent can existing guideline modeling languages capture the requirements for such a type of systems. This paper answers this research question by carrying out three case studies using three distinct and representative type of guideline languages. The expressiveness, interoperability, and maintainability of these three languages are compared against the requirements of typical computerized safety checklists. The results indicate all of these languages can partially support the requirements of computerized safety checklists. Whereas some important features of computerized checklists are not yet covered these languages.

Clinical guidelines, pathways and protocols are introduced to standardize and provide best-practice care. Analyzing deviations of actual care against the documented best practices is useful to find opportunities for complying better in the future. Prior work demonstrates that deviation analyses should be accurate, automated and insightful but only few studies manage to satisfy all three intentions. In this paper, we manage to reconcile accuracy with automation and insightfulness by combining the previously disconnected steps of checking and mining in compliance analysis software. Results are achieved using an algorithm that consists of three steps. We demonstrate the effectiveness of the algorithm via a real-life case from the intensive care unit of a Dutch hospital.

The meaning of the term 'Big Data' is still subject to debate, in spite of being widely used in biomedical publications. This confusion in definition leads to missed opportunities for peers to exchange knowledge and practices. A better understanding of Big Data may help researchers to identify themselves with the Big Data community. In this study, we investigate the most distinguishing features in 'Big Data'-labelled publications by comparing them against publication without this label (non-Big Data), using text mining and machine learning methods. Furthermore, the usage of the term Big Data was analysed over time. Our models could successfully make a distinction between publications labelled with 'Big Data' and those without. The most distinguishing features consisted of terms such as 'omics', 'computing', 'storage', and 'mining'. We observed that publications that do not use the term Big Data may also address topics that fall under well-accepted definitions of Big Data. Trends suggest that, while the use of the term Big Data increased, it is used less reliably now as compared to earlier years.

DNA read alignment is a major step in genome analysis. However, as DNA reads continue to become longer, new approaches need to be developed to effectively use these longer reads in the alignment process. Modern aligners commonly use a two-step approach for read alignment: 1. seeding, 2. extension. In this paper, we have investigated various seeding and extension techniques used in modern DNA read alignment algorithms to find the best seeding and extension combinations. We developed an open source generic DNA read aligner that can be used to compare the alignment accuracy and total execution time of different combinations of seeding and extension algorithms. For extension, our results show that local alignment is the best extension approach, achieving up to 3.6x more accuracy than other extension techniques, for longer reads. For seeding, if BLAST-like seed extension is used, the best seeding approach is identifying all SMEMs in the DNA read (e.g., approach used by BWA-MEM). This combination is up to 6x more accurate than other seeding techniques, for longer reads. With local alignment, we observed that the seeding technique does not impact the alignment accuracy. Furthermore, we showed that an optimized implementation of local alignment using vector instructions, enabling 4.5x speedup, makes it the fastest of all extension techniques. Overall, we show that using local alignment with non-overlapping maximal exact matching seeds is the best seeding-extension combination due to its high accuracy and higher potential for optimization/acceleration for future DNA reads.

Hardware-in-the-loop simulation is an efficient method for research on radar system.Target’s echo which offered by the simulator should be synchronized with radar on frequency, time, and range bin. However, because simulator needs to take into account of the requirements of various types of radar, it is difficult to make the clock of simulator synchronized with the clock of radar. To solve the problem, synchronous sampling deviation model is established. Influence of sampling deviation on imaging is analyzed. An engineering method is put forward to eliminate the sampling deviation. This method not only provides a reference for simulation system, but also provides a reference for the design of radar system.

Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) classifier is one of the most well known interval methods, and has demonstrated strong performance as well as relative speed in training and predictions. However, recent advances in other approaches have left TSF behind. TSF originally summarises intervals using three simple summary statistics. The ‘catch22' feature set of 22 time series features was recently proposed to aid time series analysis through a concise set of diverse and informative descriptive characteristics. We propose combining TSF and catch22 to form a new classifier, the Canonical Interval Forest (CIF). We outline additional enhancements to the training procedure, and extend the classifier to include multivariate classification capabilities. We demonstrate a large and significant improvement in accuracy over both TSF and catch22, and show it to be on par with top performers from other algorithmic classes. By upgrading the interval-based component from TSF to CIF, we also demonstrate a significant improvement in the hierarchical vote collective of transformation-based ensembles (HIVE-COTE) that combines different time series representations. HIVE-COTE using CIF is significantly more accurate on the UCR archive than any other classifier we are aware of and represents a new state of the art for TSC.

Road damages are of great interest for federal road authorities and their infrastructure management as well as the automated driving task and thus safety and comfort of vehicle occupants. Therefore, we are investigating the automatic detection of different types of road damages by images from a front-facing camera in the vehicle. The data basis of our work is provided by the ’IEEE BigData Cup Challenge’ and its dataset ’RDD-2020’ with a large number of labelled images from Japan, India and the Czech Republic. Our Deep Learning approach utilizes the pre-trained Faster Region Based Convolutional Neural Networks (R-CNN). In the first step, we classify the destination of the image followed by expert networks for each region. Between the explanation of our applied Deep Learning methodology, some remaining sources of errors are discussed and further, partly failed approaches during our development period are displayed, which could be of interest for future work. Our results are convincing and we are able to achieve an F1 score of 0 . 487 across all regions for longitudinal and lateral cracks, alligator cracks and potholes.

Deep learning-based technology is a good key to unlock the object detection tasks in our real world. By using deep neural networks, we could break a problem that is dangerous and very time-consuming but has to be done every day like detecting the road state. This paper describes the solution using YOLO to detect the various types of road damage in the IEEE BigData Cup Challenge 2020. Our YOLOv5x based-solution is light-weight and fast, even it has good accuracy. We achieved an F1 score of 0.58 using our ensemble model with TTA, and it could be an adequate candidate for detecting real road damage in real-time.

The graph data structure offers a highly expressive way of representing many real-world constructs such as social networks, chemical compounds, the world wide web, street maps, etc. In essence, any collection of entities and the relationships between them can be modelled using a graph, thus preserving more information about the real-world objects than a simple vector space model. An issue that arises when operating on collections of graphs, however, is that most statistical analysis and machine learning methods expect their input data to be in the form of multidimensional vectors, where all items can be compared with each other using well-understood metrics such as Euclidean or Manhattan distance. This paper presents a variety of approaches for computing distances between graphs with known node correspondence, with the aim of applying those measures alongside clustering algorithms to discover patterns in a given dataset. The performance of each distance measure is then evaluated through its ability to identify communities of graphs with similar features. We show that because the considered distance metrics highlight different structural properties, the method that produces the highest quality result will depend on the characteristics of the processed graph population.

A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0 [13], a modern data standard for collaborative science across the neurophysiology community.

Today, scientific experiments and simulations produce massive amounts of heterogeneous data that need to be stored and analyzed. Given that these large datasets are stored in many files, formats and locations, how can scientists find relevant data, duplicates or similarities? In this context, we concentrate on developing algorithms to compare similarity of time series for the purpose of search, classification and clustering. For example, generating accurate patterns from climate related time series is important not only for building models for weather forecasting and climate prediction, but also for modeling and predicting the cycle of carbon, water, and energy. We developed the methodology and ran an exploratory analysis of climatic and ecosystem variables from the FLUXNET2015 dataset. The proposed combination of similarity metrics, nonlinear dimension reduction, clustering methods and validity measures for time series data has never been applied to unlabeled datasets before, and provides a process that can be easily extended to other scientific time series data. The dimensionality reduction step provides a good way to identify the optimum number of clusters, detect outliers and assign initial labels to the time series data. We evaluated multiple similarity metrics, in terms of the internal cluster validity for driver as well as response variables. While the best metric often depends on a number of factor, the Euclidean distance seems to perform well for most variables and also in terms of computational expense.

Energy systems are evolving towards 100% green energy production. The share of green energy in electrical distribution systems is progressively increasing, implying also an increment on the number of renewable energy units in the low-voltage grid. Following this trend, thousands of consumers connected to the power grid in a decentralized manner become small producers, changing the traditional paradigm of energy distribution from top to bottom. Currently, the modern Advanced Metering Infrastructure (AMI) enables the possibility of collecting several types of status data from the end-users which Distribution System Operators (DSOs) can use to their advantage to optimize management and planning operations. As a part of this optimization, having a spatial overview over the low-voltage grid can speed up the monitoring processes and allows to obtain a real-time insight on what is happening in the grid, compared to the traditionally used analysis methods. Many business structures for smart grid cyber physical systems are looking into how to integrate advanced data management models. Such models should provide the means for obtaining meaningful data visualization where only the relevant data is timely processed, filtered and visualized for the operators to efficiently react to grid anomalies in real-time.The purpose of this paper is to investigate how to efficiently design a monitoring/visualization system for low-voltage electrical grids based on the DSOs’ needs and feedback. The proposed system implementation stands on emulating an existing geographic scenario by a virtual AMI integration. The efficiency of the prototype is evaluated versus the traditional monitoring operations derived from user experience studies, such as a reduction in time to perform a specific anomaly detection operation. Furthermore, the advantages of spatial awareness are meant to further strengthen the motivation for integrating measurements into a Geographic Information System (GIS) environment.

The use of quantum computing in graph community detection and regularity checking related to Szemerédi's Regularity Lemma (SRL) are demonstrated with D-Wave Systems' quantum annealer and simulations. We demonstrate the capability of quantum computing in solving hard problems relevant to big data. A new community detection algorithm based on SRL is also introduced and tested.

Natural Language Inference (NLI) is a fundamental step towards natural language understanding. The task aims to detect whether a premise entails or contradicts a given hypothesis. NLI contributes to a wide range of natural language understanding applications such as question answering, text summarization and information extraction. Recently, the public availability of big datasets such as Stanford Natural Language Inference (SNLI) and SciTail, has made it feasible to train complex neural NLI models. Particularly, Bidirectional Long Short-Term Memory networks (BiLSTMs) with attention mechanisms have shown promising performance for NLI. In this paper, we propose a Combined Attention Model (CAM) for NLI. CAM combines the two attention mechanisms: intra-attention and inter-attention. The model first captures the semantics of the individual input premise and hypothesis with intra-attention and then aligns the premise and hypothesis with inter-sentence attention. We evaluate CAM on two benchmark datasets: Stanford Natural Language Inference (SNLI) and SciTail, achieving 86.14% accuracy on SNLI and 77.23% on SciTail. Further, to investigate the effectiveness of individual attention mechanism and in combination with each other, we present an analysis showing that the intra- and inter-attention mechanisms achieve higher accuracy when they are combined together than when they are independently used.

Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare.

The content generated by users on social media is rich in personal information that can be mined to construct accurate user profiles, and subsequently used for tailored advertising or other personalized services. Facebook has recently come under scrutiny after a third party gained access to the data of millions of users and mined it to construct psychographical profiles, which were allegedly used to influence voters in elections. As part of a possible solution to avoid data breaches while still being able to perform meaningful machine learning (ML) on social media data, we propose a privacy-preserving algorithm for k-nearest neighbor (kNN) [1] , one of the oldest ML methods, used traditionally in collaborative filtering recommender systems.

The combination of Condition Based monitoring techniques with the predictive capabilities of neural networks represents a topic of central importance when it comes to maximizing production profits and consequently reducing costs and downtime. The ability to plan the best strategy based on the prediction of potential damaging events can represent a significant contribution, especially for the maintenance function. In fact, optimization of the management of the equipment is a fundamental step to guarantee the competitiveness of companies in the current market. In this paper, a tool based on the implementation of Radial Basis Function Neural Networks was developed to support the maintenance function in the decision-making process. In addition to providing an indication of the status of the equipment, the current approach provides an additional level of information in terms of predicting the confidence interval around the prediction of the neural network. The confidence interval combined with the prediction of the future state of the equipment can be of fundamental importance in order to avoid strategic decisions based on a low level knowledge of the system status or prediction performance of the applied algorithm. The developed tool is tested on the prediction of a naval propulsion system gas turbine performance decay, where the statuses of both the turbine and the compressor of the system are predicted as well as predicting their confidence intervals.

Many machine learning (ML) applications rely on large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data. The emergence of consumer-grade, low-cost brain-computer interfaces (BCIs) and corresponding software development kits1 is bringing the use of BCI within reach of application developers. The access that BCI applications have to neural signals rightly raises privacy concerns. Application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other personal data [1]. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users.

Statistical analysis of large and sparse graphs is a challenging problem in data science due to the high dimensionality and nonlinearity of the problem. This paper presents a fast and scalable algorithm for partitioning such graphs into disjoint groups based on observed graph distances from a set of reference nodes. The resulting partition provides a low-dimensional approximation of the full distance matrix which helps to reveal global structural properties of the graph using only small samples of the distance matrix. The presented algorithm is inspired by the information-theoretic minimum description principle. We investigate the performance of this algorithm for selected real data sets and for synthetic graph data sets generated using stochastic block models and power-law random graphs, together with analytical considerations for sparse stochastic block models with bounded average degrees., Comment: IEEE BigData 2018 Conference Workshop, Advances in High Dimensional Big Data, 10.-13.12. 2018, Seattle USA

A method for compression of large graphs and matrices to a block structure is further developed. Szemer\'edi's regularity lemma is used as a generic motivation of the significance of stochastic block models. Another ingredient of the method is Rissanen's minimum description length principle (MDL). We continue our previous work on the subject, considering cases of missing data and scaling of algorithms to extremely large size of graphs. In this way it would be possible to find out a large scale structure of a huge graphs of certain type using only a tiny part of graph information and obtaining a compact representation of such graphs useful in computations and visualization., Comment: Accepted for publication in: Fourth International Workshop on High Performance Big Graph Data Management, Analysis, and Mining, December 11, 2017, Bosto U.S.A

The recent boom of big data, coupled with the challenges of its processing and storage gave rise to the development of distributed data processing and storage paradigms like MapReduce, Spark, and NoSQL databases. With the advent of cloud computing, processing and storing such massive datasets on clusters of machines is now feasible with ease. However, there are limited tools and approaches, which users can rely on to gauge and comprehend the performance of their big data applications deployed locally on clusters, or in the cloud. Researchers have started exploring this area by providing benchmarking suites suitable for big data applications. However, many of these tools are fragmented, complex to deploy and manage, and do not provide transparency with respect to the monetary cost of benchmarking an application. In this paper, we present Plug And Play Bench, an infrastructure aware abstraction built to integrate and simplify the deployment of big data benchmarking tools on clusters of machines. PAPB automates the tedious process of installing, configuring and executing common big data benchmark workloads by containerising the tools and settings based on the underlying cluster deployment framework. Our proof of concept implementation utilises HiBench as the benchmark suite, HDP as the cluster deployment framework and Azure as the cloud platform. The paper further illustrates the inclusion of cost metrics based on the underlying Microsoft Azure cloud platform., Comment: 8 pages, Published as a workshop paper in 2017 IEEE International Conference on Big Data in Boston Dec 11 - 14

The classification of graphs is a key challenge within many scientific fields using graphs to represent data and is an active area of research. Graph classification can be critical in identifying and labelling unknown graphs within a dataset and has seen application across many scientific fields. Graph classification poses two distinct problems: the classification of elements within a graph and the classification of the entire graph. Whilst there is considerable work on the first problem, the efficient and accurate classification of massive graphs into one or more classes has, thus far, received less attention. In this paper we propose the Deep Topology Classification (DTC) approach for global graph classification. DTC extracts both global and vertex level topological features from a graph to create a highly discriminate representation in feature space. A deep feed-forward neural network is designed and trained to classify these graph feature vectors. This approach is shown to be over 99% accurate at discerning graph classes over two datasets. Additionally, it is shown to be more accurate than current state of the art approaches both in binary and multi-class graph classification tasks.

The Finnish 5G test network (5GTN) is built to support development and analysis of 5G technologies and new services on top of them. 5GTN includes a holistic monitoring and test generation architecture, enabling generation and collection of diverse test and datasets, analyzing the results and using these as basis for developing new technologies, algorithms, services, and optimizations. Both the combined network and service parameters space, as well as data volume, provide us with several big data problems. In this paper, we describe the overall system architecture, the types of data we collect, the initial analysis we have performed on the data, and what we have learned so far. The network is constantly evolving with new services integrated and tested, and new big data analytics being applied as we learn from our tests. As such, these are preliminary results for ongoing work.

Recent technological advances in modern healthcare have lead to the ability to collect a vast wealth of patient monitoring data. This data can be utilised for patient diagnosis but it also holds the potential for use within medical research. However, these datasets often contain errors which limit their value to medical research, with one study finding error rates ranging from 2.3%???26.9% in a selection of medical databases. Previous methods for automatically assessing data quality normally rely on threshold rules, which are often unable to correctly identify errors, as further complex domain knowledge is required. To combat this, a semantic web based framework has previously been developed to assess the quality of medical data. However, early work, based solely on traditional semantic web technologies, revealed they are either unable or inefficient at scaling to the vast volumes of medical data. In this paper we present a new method for storing and querying medical RDF datasets using Hadoop Map / Reduce. This approach exploits the inherent parallelism found within RDF datasets and queries, allowing us to scale with both dataset and system size. Unlike previous solutions, this framework uses highly optimised (SPARQL) joining strategies, intelligent data caching and the use of a super-query to enable the completion of eight distinct SPARQL lookups, comprising over eighty distinct joins, in only two Map / Reduce iterations. Results are presented comparing both the Jena and a previous Hadoop implementation demonstrating the superior performance of the new methodology. The new method is shown to be five times faster than Jena and twice as fast as the previous approach.

We propose a method for finding CRMs in a set of co-regulated genes. Each CRM consists of a set of binding sites of transcription factors. We wish to find CRMs involving the same transcription factors in multiple sequences. Finding such a combination of transcription factors is inherently a combinatorial problem. We solve this problem by combining the principles of itemset mining and constraint programming. The constraints involve the putative binding sites of transcription factors, the number of sequences in which they co-occur and the proximity of the binding sites. Genomic background sequences are used to assess the significance of the modules. We experimentally validate our approach and compare it with state-of-the-art techniques. acceptance rate = 17.2% ispartof: pages:363-368 ispartof: Proc. of IEEE International Conference on Bioinformatics & Biomedicine pages:363-368 ispartof: IEEE International Conference on Bioinformatics & Biomedicine location:Hong Kong date:18 Dec - 21 Dec 2010 status: published

With more and more large networks becoming available, mining and querying such networks are increasingly impor- tant tasks which are not being supported by database models and querying languages. This paper wants to alleviate this situation by proposing a data model and a query language for facilitating the analysis of networks. Key features in- clude support for executing external tools on the networks, flexible contexts on the network each resulting in a different graph, primitives for querying subgraphs (including paths) and transforming graphs. The data model provides for a closure property, in which the output of every query can be stored in the database and used for further querying. Acceptance rate: 14,5% ispartof: pages:485-494 ispartof: Proceedings of the ACM Conference on Information and Knowledge Management pages:485-494 ispartof: ACM Conference on Information and Knowledge Management location:Hong Kong, China date:2 Nov - 6 Nov 2009 status: published

Community Question Answering (CQA) has emerged as a popular type of service meeting a wide range of information needs. Such services enable users to ask and answer questions and to access existing question-answer pairs. CQA archives contain very large volumes of valuable user-generated content and have become important information resources on the Web. To make the body of knowledge accumulated in CQA archives accessible, effective and efficient question search is required. Question search in a CQA archive aims to retrieve historical questions that are relevant to new questions posed by users. This paper proposes a category-based framework for search in CQA archives. The framework embodies several new techniques that use language models to exploit categories of questions for improving question-answer search. Experiments conducted on real data from Yahoo! Answers demonstrate that the proposed techniques are effective and efficient and are capable of outperforming baseline methods significantly.

Efficient protection of huge amount of IoT produced data is key for wide scale data analytic services. The most efficient way is to use pure symmetric encryption as that allows both fast decryption at the analytic engine side as well as energy efficient encryption at the IoT side. However, symmetric encryption can only be performed if there is a way to directly map an encrypted object to the correct key. Typically, such mapping require a unique IoT identity, which constitute a privacy problem. In this paper, we present an IoT identity protection scheme for symmetric IoT data encryption. We give basic security definitions for this problem setting, present a new construction and give security proofs of security level achieved with the construction. Performance figures for a proof of concept implementation are also given. The new scheme gives a fair trade-off between identity privacy and complexity.