High Data Availability Research Articles

White Rabbit HSR: A Seamless Subnanosecond Redundant Timing System With Low-Latency Data Capabilities for the Smart Grid

New smart grid and industrial-Internet-of-things (IIoT) directions require accurate synchronization features to capture, trigger, and manage events in industrial networks with the best possible precision and accuracy. The implementation of redundancy features for time and data distribution to increase fault tolerance, together with an enhancement of the availability of the network services such as generic object-oriented substation events, become mandatory due to their critical nature. For these reasons, the authors of this paper presented a new challenging approach covering the industrial standards requirements regarding timing and availability through the development of mechanisms that make possible the avoidance of single point of failure in ring topologies. This approach uses the White Rabbit (WR) as the main timing distribution technology, able to synchronize devices below 1 ns. Moreover, the development of a mechanism able to change from a primary to a backup time reference in approximately zero time with an observed maximum phase shift of 170 ps implies an important qualitative leap with respect to IIoT and the smart grid (e.g., phasor measurement units). This paper addresses the development and results of the high-availability seamless redundancy protocol for the WR technology to conform a leading-edge deterministic and reliable ultraaccurate timing system with high-availability data features for industrial facilities in accordance with IEC 61850 and IEC 62439-3.

Implementation of Microsoft SQL Server using ‘AlwaysOn’ for High Availability and Disaster Recovery without Shared Storage

It is important for any organization to handle increase in data; normally this is the job of the DBA in organization to take care of this data growth, along with its protection and its availability. High availability of data is also most important for organizations, for that purpose, shared storage is usually the solution, for example, SAN or NAS storage as Microsoft Cluster Database instances. Mostly Organization does not have shared storage in their infrastructure, which was required for Microsoft cluster SQL database instance; due to that they used other native methods to implement like log shipping or database mirroring, which was not efficient method for high-availability and disaster-recovery solution. After that, Microsoft introduced native method called SQL AlwaysOn method that could be implemented without using SAN and NAS storage method to implement high availability and disaster recovery without using shared storage. SQL AlwaysOn option is using only Microsoft cluster service and shared folder between the nodes. This paper written for DBA and System Administrators; and is implementation of Microsoft SQL Database tier step by step.

Implementation of Microsoft SQL Server using ‘AlwaysOn’ for High Availability and Disaster Recovery without Shared Storage

It is important for any organization to handle increase in data; normally this is the job of the DBA in organization to take care of this data growth, along with its protection and its availability. High availability of data is also most important for organizations, for that purpose, shared storage is usually the solution, for example, SAN or NAS storage as Microsoft Cluster Database instances. Mostly Organization does not have shared storage in their infrastructure, which was required for Microsoft cluster SQL database instance; due to that they used other native methods to implement like log shipping or database mirroring, which was not efficient method for high-availability and disaster-recovery solution. After that, Microsoft introduced native method called SQL AlwaysOn method that could be implemented without using SAN and NAS storage method to implement high availability and disaster recovery without using shared storage. SQL AlwaysOn option is using only Microsoft cluster service and shared folder between the nodes. This paper written for DBA and System Administrators; and is implementation of Microsoft SQL Database tier step by step.

1990–2016 surface solar radiation variability and trend over the Piedmont region (northwest Italy)

A new surface solar radiation database of 74 daily series is set up for the Piedmont region (northwest Italy) for the 1990–2016 period. All the series are subjected to a detailed quality control, homogenization and gap-filling procedure and are transformed into relative annual/seasonal anomaly series. Finally, a gridded version (0.5°×0.5°) of the database is generated. The resulting series show an increasing tendency of about + 2.5% per decade at annual scale, with strongest trend in autumn (+ 4% per decade). The only exception is winter, showing a negative but not significant trend. Considering the plain and mountain mean series, the trends are more intense for low than for high elevations with a negative vertical gradient of about − 0.03% per decade per 100 m at annual scale and values up to − 0.07% per decade per 100 m in spring. Focusing on clear days only (selected by CM SAF ClOud fractional cover dataset from METeosat first and second generation—Edition 1 satellite data over the 1991–2015 period), trend significance strongly increases and both low and high elevation records exhibit a positive trend in all seasons. However, the trends result slightly lower than for all-sky days (with the only exception of winter). The differences observed under clear-sky conditions between low and high elevations are more pronounced in winter, where the trend shows a negative vertical gradient of about − 0.1% per decade every 100 m. Overall, this paper shows how a high station density allows performing a more detailed quality control thanks to the higher performances in detecting the inhomogeneities with higher data availability and capturing regional peculiarities otherwise impossible to observe.

A cross-sectional ecological analysis of international and sub-national health inequalities in commercial geospatial resource availability

BackgroundCommercial geospatial data resources are frequently used to understand healthcare utilisation. Although there is widespread evidence of a digital divide for other digital resources and infra-structure, it is unclear how commercial geospatial data resources are distributed relative to health need.MethodsTo examine the distribution of commercial geospatial data resources relative to health needs, we assembled coverage and quality metrics for commercial geocoding, neighbourhood characterisation, and travel time calculation resources for 183 countries. We developed a country-level, composite index of commercial geospatial data quality/availability and examined its distribution relative to age-standardised all-cause and cause specific (for three main causes of death) mortality using two inequality metrics, the slope index of inequality and relative concentration index. In two sub-national case studies, we also examined geocoding success rates versus area deprivation by district in Eastern Region, Ghana and Lagos State, Nigeria.ResultsInternationally, commercial geospatial data resources were inversely related to all-cause mortality. This relationship was more pronounced when examining mortality due to communicable diseases. Commercial geospatial data resources for calculating patient travel times were more equitably distributed relative to health need than resources for characterising neighbourhoods or geocoding patient addresses. Countries such as South Africa have comparatively high commercial geospatial data availability despite high mortality, whilst countries such as South Korea have comparatively low data availability and low mortality. Sub-nationally, evidence was mixed as to whether geocoding success was lowest in more deprived districts.ConclusionsTo our knowledge, this is the first global analysis of commercial geospatial data resources in relation to health outcomes. In countries such as South Africa where there is high mortality but also comparatively rich commercial geospatial data, these data resources are a potential resource for examining healthcare utilisation that requires further evaluation. In countries such as Sierra Leone where there is high mortality but minimal commercial geospatial data, alternative approaches such as open data use are needed in quantifying patient travel times, geocoding patient addresses, and characterising patients’ neighbourhoods.

A new Prefetching-aware Data Replication to decrease access latency in cloud environment

Data replication is an effective technique that decreases retrieval time, thus reducing energy consumption in Cloud. When necessary files aren't locally available, they will be fetched from remote locations that is very high-time consuming process. Therefore, it is superior to pre-replicate the popular files. Even though few previous works considered prediction-based replication strategy, the prediction is not precise at many situations and occupies the storage. To address these challenges, a new dynamic replication strategy called Prefetching-aware Data Replication (PDR) is proposed, which determines the correlation of the data files using the file access history and pre-fetches the most popular files. So, the next time that this site requires a file, it will be locally available. In addition, due to the storage space restriction, replica replacement strategy plays a vital role. PDR strategy can ascertain the importance of valuable replicas based on the fuzzy inference system with four input parameters (i.e., number of accesses, cost of replica, the last time the replica was accessed, and data availability). Extensive experiments with CloudSim show that PDR achieves high data availability, high hit ratio, low storage and bandwidth consumption. On average PDR reduces over 35% of response time when compared to the other algorithms.

Plant invasions in Italy: An integrative approach using the European LifeWatch infrastructure database

By using the LifeWatch database – a European e-science infrastructure on native and alien plant occurrence collected from a wide set of Italian terrestrial sites – we estimated the occurrence of alien species in a cross-habitat framework in relation to propagule pressure (P), abiotic (A) and biotic (B) conditions. The research represents an example of macro-ecological assessment of invasion risk at national scale claimed by the recent European regulation (EU 1143/2014) on invasive alien species. Based on a large vegetation dataset, we estimated alien and native species richness across a set of 19 Italian terrestrial sites. By using a Generalized Linear Mixed Model, we investigated the relationship between the proportion of alien occurrence across sites and habitat types (EUNIS) at family level with PAB putative drivers derived from LifeWatch and other open access geographic databases. Our results support the full model as the best-fitting option, highlighting that plant invasion in the Italian terrestrial ecosystems is a function of the combination of PAB conditions. In the first step of the invasion process, the accessibility time from towns plays a major role. By contrast, the abiotic filter imposed by environmental condition (high temperatures and low precipitations) as well the competition with the native community (high species richness) may pose a limit to the settlement and spread of alien species. Because of the high availability of similar data on PAB conditions worldwide, this study represents an effective and easy tool to design appropriate biodiversity conservation policies focused on the prevention of alien spread.

Efficient Disk-Based Directed Graph Processing: A Strongly Connected Component Approach

Recently, there have been many disk-based systems proposed for iterative graph processing. In the popular vertex/edge-centric systems, an iterative directed graph algorithm needs to reprocess many partitions so as to update their vertices’ states according to other non-convergent vertices for the unawareness of their dependencies. As a result, it induces high data access cost and a long time to converge. To tackle this problem, we propose a novel system for iterative directed graph processing with taking advantage of the strongly connected component (SCC) structure. It stores a directed graph into a directed acyclic graph (DAG) sketch, with each node representing a SCC in the original data graph. During execution, the SCCs are loaded into memory for processing in a parallel way according to the topological order of the DAG sketch, and the vertices in each SCC are tried to be handled along the directed paths. In this way, each SCC is able to reach convergence in order and needs to be loaded into the main memory for exactly once, getting much lower data access cost and faster convergence. Besides, the vertices of each SCC need fewer updates for convergence. We further develop a lightweight approach to maintain the DAG sketch and handle SCCs in an incremental way for evolving graphs. Compared with the state-of-the-art methods, experimental results show that our approach achieves a performance improvements of 1.46-8.37 times for static graphs, and can reduce the execution time by 61.4-72.7 percent for evolving graphs.

New tree-ring evidence for the Late Glacial period from the northern pre-Alps in eastern Switzerland

The rate and magnitude of temperature variability at the transition from the Last Glacial Maximum into the early Holocene represents a natural analog to current and predicted climate change. A limited number of high-resolution proxy archives, however, challenges our understanding of environmental conditions during this period. Here, we present combined dendrochronological and radiocarbon evidence from 253 newly discovered subfossil pine stumps from Zurich, Switzerland. The individual trees reveal ages of 41–506 years and were growing between the Allerød and Preboreal (∼13′900–11′300 cal BP). Together with previously collected pines from this region, this world's best preserved Late Glacial forest substantially improves the earliest part of the absolutely dated European tree-ring width chronology between 11′300 and 11′900 cal BP. Radiocarbon measurements from 65 Zurich pines between ∼12′320 and 13′950 cal BP provide a perspective to prolong the continuous European tree-ring record by another ∼2000 years into the Late Glacial era. These data will also be relevant for pinpointing the Laacher See volcanic eruption (∼12′900 cal BP) and two major Alpine earthquakes (∼13′770 and ∼11′600 cal BP). In summary, this study emphasizes the importance of dating precision and multi-proxy comparison to disentangle environmental signals from methodological noise, particularly during periods of high climate variability but low data availability, such as the Younger Dryas cold spell (∼11′700 and 12′900 cal BP).

Clustering West Nile Virus Spatio-temporal data using ST-DBSCAN

Spatio-temporal data mining has been the talk of the day due to high availability of spatio-temporal data from varied sources in diverse fields. Through many tracking devices, huge amounts of spatio-temporal data are being generated. In epidemiology, diseases, patterns and trends attached can be explored taking advantage of methods such as spatio-temporal clustering to discover new knowledge. In this paper Spatio-Temporal Density Based Spatial Clustering of Applications with Noise (ST-DBSCAN) is implemented and analysed on a public health dataset. Upon the implementation, results are analysed, loopholes spotted and a fuzzy version of ST-DBSCAN is proposed. The method is successfully applied to find spatio-temporal clusters in Chicago West Nile Virus (WNV) surveillance data for the period 2007 to 2017.The drawbacks in the original ST-DBSCAN are identified and solutions are proposed. ST-DBSCAN is an extension of the original Density Based Spatial Clustering of Applications with Noise (DBSCAN).

Comparing Speed Data from Stationary Detectors Against Floating-Car Data

This paper compares speed data measured by induction loops of stationary detectors with reported speeds from floating-car data, which are based on most recent GPS observations of probe vehicles. Detector data are aggregated over a one minute time interval and that means a 30 s delay occurs on average. The time delay issues with respect to floating-car data are quite convoluted with many influences: (i) the update frequencies from vehicles to the backend server, (ii) the fleet size of floating cars, (iii) the current traffic flow, and (iv) the provider treatment. The floating-car dataset has a high spatial resolution with an average segment length of 100 m suited for large-scale traffic observation and management. The spatial dimension of detector data can only be reconstructed ex-post from spotty positions (mean detector positions distance approx. 1.3 km).The paper analyzes which source is more advantageous in terms of detecting traffic jams, high temporal availability of detector data or detailed spatial resolution of floating-car data. An algorithm is presented to compute the jam detection duration, which means we are able to recognize which data source detects the jam earliest. The results demonstrate that there exist regions along certain road stretches where floating-car data clearly outperform stationary data. However, in regions where detectors are densely placed, stationary sensor data recognize a jam situation approx. 2 min earlier than floating-car based speed data.The datasets cover a period of 80 days in 2015 for both driving directions on the German autobahn A9 in the north of Munich.

Data Modeling Guidelines for NoSQL Document-Store Databases

Good database design is key to high data avail-ability and consistency in traditional databases, and numerous techniques exist to abet designers in modeling schemas appropri-ately. These schemas are strictly enforced by traditional database engines. However, with the emergence of schema-free databases (NoSQL) coupled with voluminous and highly diversified datasets (big data), such aid becomes even more important as schemas in NoSQL are enforced by application developers, which requires a high level of competence. Precisely, existing modeling techniques and guides used in traditional databases are insufficient for big-data storage settings. As a synthesis, new modeling guidelines for NoSQL document-store databases are posed. These guidelines cut across both logical and physical stages of database designs. Each is developed based on solid empirical insights, yet they are prepared to be intuitive to developers and practitioners. To realize this goal, we employ an exploratory approach to the investigation of techniques, empirical methods and expert consultations. We analyze how industry experts prioritize requirements and analyze the relationships between datasets on the one hand and error prospects and awareness on the other hand. Few proprietary guidelines were extracted from a heuristic evaluation of 5 NoSQL databases. In this regard, the proposed guidelines have great potential to function as an imperative instrument of knowledge transfer from academia to NoSQL database modeling practices.

A global database of polybrominated diphenyl ether flame retardant congeners in foods and supplements

Polybrominated diphenyl ether (PBDE) flame retardants contaminate the food supply yet health effects are uncertain. A global PBDE database was developed to improve diet and disease risk assessments. Congener-specific data from 2002 to 2015 were extracted from 86 articles into a source database representing 32 countries. Geometric mean PBDE concentrations for foods and supplements were derived for 11 congeners individually and combined, and used to calculate means for 27 dietary groups (pg/g ww). Dark or oily fish had the highest data availability, followed by shellfish, eggs, dairy products and dairy fats. Data were less available for white or lean fish, red meat, poultry meat, processed meats, fish oil supplements; 17 groups had very limited data. At the group level, mean ∑11PBDE was extremely high for fish oil supplements (13,862 pg/g) and high for most aquatic groups (462–837 pg/g), poultry liver, poultry fat (1045–1860 pg/g). Moderate groups included white or lean fish, poultry meat, poultry skin, eggs, baked products, red meat fat, red meat liver (115–414 pg/g). Dairy and plant groups had low PBDE concentrations. ∑11PBDE variability was high within most aquatic groups. This database supports assessment of dietary PBDE in multiple jurisdictions and identifies important sources for dietary tool inclusion and analyses.

An Achievement of High Availability and Low Cost on Data Center Infrastructure

This study is to provide a design of low-cost and high-availability data center infrastructure for small/medium-scale businesses. The basic concept is to establish an infrastructure consisting of primary and backup sides through the clustering technology. By default, the primary side is active and responsible for data switching while the backup side is standby for the failure of the primary side. The design achieves the features of 1) providing full-level high availability (HA) at network, server, application, and management levels, 2) controlling the routing among network-level clusters to solve the “PPPOE connection racing” and “winding path” problems, 3) monitoring and recovering the objects in each level with an economic and effective way, and 4) handling events resulting from changing of object states in an event center. Finally, the experiment results are exhibited with five testing scenarios for verification and elaboration of the effectiveness of the HA design. The system can recover the failed objects and solve the routing problems of PPPOE connection racing and winding path among HA clusters automatically.

EFFECTIVE DATA SHARING IN MANETs USING CLUSTER BASED REPLICATION

Replication has been a feasible solution for increasing the dataavailability and improving overall system performance. In mobile adhoc networks (MANETs), a proficient replica management is achallenging problem due to its unreliability, dynamic, and unstableand resource constrained nature. The data replication improves the data availability and when employed may increase the energy consumption. In this paper, a replication method for MANETs hasbeen proposed with an aim to attain high data availability with anoptimal energy consumption in case of network disconnection or node failure and also is scalable. The data replication is based on a costfunction and also the trade-off between the communication and replication energy consumption costs is considered. The replicaselection mechanism identifies suitable number of nodes to place thereplica and takes benefit from a 1-hop cluster formation based onentropy coefficient. The energy utilization is kept minimal even withreplication by controlling the number of data replicas and exploiting the cluster structure. The designed algorithm is simulated using NS2and it has been observed that it effectively controls energyconsumption and is adaptive with respect to the rate of replication.

Morphus: Supporting Online Reconfigurations in Sharded NoSQL Systems

While sharded NoSQL stores offer high availability, reconfiguration operations present a major pain point in deployments today. For instance, in order to change a configuration setting, such as the shard (or primary) key of a database table, the prevalent solutions entail shutting down the database, exporting and re-importing the table, and restarting the database. This goes against the NoSQL philosophy of high availability of data. Our system, called Morphus, provides support toward reconfigurations for NoSQL stores in an online manner. Morphus allows read and write operations to continue concurrently with the data transfer among servers. Morphus works for NoSQL stores that feature master-slave replication, range partitioning, and flexible data placement. This paper presents: 1) a systems architecture for online reconfigurations, incorporated into MongoDB and 2) optimal algorithms for online reconfigurations. Our evaluation using realistic workloads shows that Morphus completes reconfiguration efficiently, offers high availability, and incurs low overhead for reads and writes.

Automatic Classification of Traced Neurons Using Morphological Features

The great advances in the field of neuron tracing have made possible a high availability of free-access data in the Internet, which encourages the realization of automatic classifications. The increase of neuronal reconstruction databases makes the manual classification of neurons a time-consuming and tedious task for human experts. Classification by human experts is also prone to inter- and intra-analyst variability due to the process’ inherent subjectivity. In this context, the need arises to find new descriptors having discriminative properties which allow separating the various neuron classes, and this constitutes currently an open problem. Such descriptors would contribute to improve the results of automatic classification. In this study the attention is focused on the use of new morphological features in supervised classification of traced neurons. Furthermore, we present a comparative analysis of different supervised learning algorithms oriented to the classification of reconstructed neurons. The results were validated using a non-parametric statistical test and show the usefulness of the proposed solution.

Interfaces Formed by ALD Metal Oxide Growth on Metal Layers

Redox-based resistive switching random access memory (ReRAM) has the potential to fulfill the demands of future Green IT solutions for non-volatile memory circuits with high speed data access, high reliability, and low power consumption. A single valence-change-mechanism (VCM)-type switching cell is typically built from a transition metal oxide layer sandwiched between metal electrodes of different oxidation enthalpy. It is accepted in the scientific community that oxygen exchange and drift/diffusion processes play a major role in the resistive switching process. Thus, understanding oxidation/reduction reactions taking place already during the growth process is of utmost importance. For future three-dimensional (3D) integrated arrays of ReRAM cells atomic layer deposition (ALD) will become the method of choice to grow ultra-thin, conformal, homogeneous and dense oxide films at a thermal budget below 450 °C. For todays’ two dimensional stacked crossbar arrays, the metal oxide films can be grown on nearly inert Pt electrodes, while the oxygen exchange layer built from a reactive metal is deposited on top of the metal oxide. However, in the 3D ReRAM arrays the metal oxide might need to be grown on the reactive metal as well. In this study ultrathin ALD metal oxide layers are deposited on various metal electrodes by means of in-situ transfer from the sputtered metal into the ALD chamber. For the metal layers we utilized Pt, Hf, and Ta while for the metal oxides Al2O3, TiO2, and HfO2 were chosen. ALD metal oxide processes were performed by plasma assisted ALD in an Oxford FlexAlâ system. Due to the passivation property of the dense ALD metal oxide layer the metal oxide / metal interfaces could be analyzed ex-situ if the vacuum break was kept short. Angle resolved X-ray photoemission spectroscopy studies combined with X-ray reflectivity analysis reveal a clear picture of the interface formation and the oxygen gradient formed at the interface already during growth. The influence of process parameters and stack sequences on this ‘built-in’ oxygen gradient at metal / ALD metal oxide interfaces will be covered in this presentation. Among others, the possibility for PtOx formation during remote oxygen plasma ALD processes will be discussed.

Conflict-Free Loop Mapping for Coarse-Grained Reconfigurable Architecture with Multi-Bank Memory

Coarse-grained reconfigurable architecture (CGRA) is a promising architecture with high performance, high power-efficiency and attraction of flexibility. The computation-intensive parts of an application (e.g., loops) are often mapped on CGRA for acceleration. Due to the high parallel data access demands, the architecture with multi-bank memory is proposed to improve parallelism. For CGRA with multi-bank memory, a joint solution, which simultaneously considers the memory partitioning and modulo scheduling, is proposed to achieve a valid mapping with better performance. In this solution, the modulo scheduling and operator scheduling are used to achieve a valid loop mapping and a valid data placement without any memory access conflicts. By avoiding the pipelining stalls caused by conflicts, the performance of loop mapping is greatly improved. The experimental results on benchmarks of the Livermore, Polybench and Mediabench show that our approach can improve the performance of loops on CGRA to 1.89 $\times$ , 1.49 $\times$ and 1.37 $\times$ compared with REGIMap, HTDM and REGIMap with memory partitioning, at cost of an acceptable increase in compilation time.

Hybrid Data Aggregation Technique to Categorize the Web Users to Discover Knowledge About the Web Users

Web usage mining is a knowledge discovery technique where a data analyst can discover useful information from the web users’ data. Web contains billions of web pages. The web access behaviour of one web user differs from that of another and also it changes with respect to their temporal property. By analyzing the users’ data, the web administrator can personalize the web pages according to individual web users’ interest. Personalizing the web page gives various advantages in this fast era such as low search time, less data transfer, higher availability of data, lower bandwidth traffic, targeted advertisement and identifying the threaded web users and high web users satisfaction. Due to the above advantages, it is very much essential in the present World Wide Web. Various algorithms, techniques and tools are available in the field of web usage mining. Although there are various techniques, algorithms and tools developed related to web usage mining, new techniques are required to make the discovery of knowledge more accurate. In this paper, a novel technique is proposed by using various methods such as web log, web ranking, web rating and web review based method to identify the success rate of various web pages and summarize the value to identify the accurate success rate of each web page. The success rate is normalized and aggregated into three categories for personalizing the web user. Personalizing the web user based on grouping relevant web access behaviour reduces the calculation complexity. It is very effective in very large websites. This technique is very much effective for analyzing the outreach of web advertisement to the web users.