Access methods for multiversion data

The importance of multidimensional index structures to numerous emerging database applications is well established. However, before these index structures can be supported as access methods (AMs) in a “commercial-strength” database management system (DBMS), efficient techniques to provide transactional access to data via the index structure must be developed. Concurrent accesses to data via index structures introduce the problem of protecting ranges specified in the retrieval from phantom insertions and deletions (the phantom problem ). This paper presents a dynamic granular locking approach to phantom protection in Generalized Search Trees(GiSTs), an index structure supporting an extensible set of queries and data types. The granular locking technique offers a high degree of concurrency and has a low lock overhead. Our experiments show that the granular locking technique (1) scales well under various system loads and (2) similar to the B-tree case, provides a significantly more efficient implementation compared to predicate locking for multidimensional AMs as well. Since a wide variety of multidimensional index structures can be implemented using GiST, the developed algorithms provide a general solution to concurrency control in multidimensional AMs. To the best of our knowledge, this paper provides the first such solution based on granular locking.

Similarity search is fundamental to store and retrieve large volumes of complex data required by many real world applications. A useful mechanism for such concept is the query-by-similarity. Based on their topological properties, metric similarity functions can be used to index sets of data which can be queried effectively and efficiently by the so-called metric access methods. However, data produced by various application domains and the varying data types handled often lead to missing data, hence, they do not follow the metric similarity requirements. As a consequence, missing data cause distortions in the index structure and yield bias in the query answer. In this paper, we propose the Hollow-tree, a novel access method aimed at successfully retrieving data with missing attribute values. It employs new strategies for indexing and searching data elements, capable of handling the missing data issues when the cause of missingness is ignorable. The indexing strategy is based on a family of distance functions that allow measuring the distance between elements with missing values, along with a set of policies able to organize the elements in the index without causing distortions to its internal structure. The searching strategy employs fractal dimension property of the data to achieve accurate query answer while considering data with missing values part of the response. Results from experiments performed on a variety of real and synthetic data sets showed that, while other metric access methods deteriorate with small amounts of missing values, the Hollow-tree maintains a remarkable performance with almost 100% of precision and recall for range queries and more than 90% for k-nearest neighbor queries, for up to 40% of missing values.

Objective. Intracortical brain–machine interfaces (iBMIs) aim to restore efficient communication and movement ability for paralyzed patients. However, frequent recalibration is required for consistency and reliability, and every recalibration will require relatively large most current sample set. The aim in this study is to develop an effective decoder calibration method that can achieve good performance while minimizing recalibration time. Approach. Two rhesus macaques implanted with intracortical microelectrode arrays were trained separately on movement and sensory paradigm. Neural signals were recorded to decode reaching positions or grasping postures. A novel principal component analysis-based domain adaptation (PDA) method was proposed to recalibrate the decoder with only ultra small current sample set by taking advantage of large historical data, and the decoding performance was compared with other three calibration methods for evaluation. Main results. The PDA method closed the gap between historical and current data effectively, and made it possible to take advantage of large historical data for decoder recalibration in current data decoding. Using only ultra small current sample set (five trials of each category), the decoder calibrated using the PDA method could achieve much better and more robust performance in all sessions than using other three calibration methods in both monkeys. Significance. (1) By this study, transfer learning theory was brought into iBMIs decoder calibration for the first time. (2) Different from most transfer learning studies, the target data in this study were ultra small sample set and were transferred to the source data. (3) By taking advantage of historical data, the PDA method was demonstrated to be effective in reducing recalibration time for both movement paradigm and sensory paradigm, indicating a viable generalization. By reducing the demand for large current training data, this new method may facilitate the application of intracortical brain–machine interfaces in clinical practice.

Aim Niche conservatism is key to understanding species responses to environmental stress such as climate change or arriving in new geographical space such as biological invasion. Halotydeus destructor is an important agricultural pest in Australia and has been the focus of extensive surveys that suggest this species has undergone a niche shift to expand its invasive range inland to hotter and drier environments. We employ modern correlative modelling methods to examine niche conservatism in H. destructor and highlight ecological differences between historical and current distributions.Location Australia and South Africa.Methods We compile comprehensive distribution data sets for H. destructor, representing the native range in South Africa, its invasive range in Australia in the 1960s (40 yr post‐introduction) and its current range in Australia. Using MAXENT, we build correlative models and reciprocally project them between South Africa and Australia and investigate range expansion with models constructed for historical and current data sets. We use several recently developed model exploration tools to examine the climate similarity between native and invasive ranges and subsequently examine climatic variables that limit distributions.Results The invasive niche of H. destructor in Australia transgresses the native niche in South Africa, and the species has expanded in Australia beyond what is predicted from the native distribution. Our models support the notion that H. destructor has undergone a more recent range shift into hotter and drier inland areas of Australia since establishing a stable distribution in the 1960s.Main conclusions Our use of historical and current data highlights that invasion is an ongoing dynamic process and demonstrates that once a species has reached an established range, it may still expand at a later stage. We also show that model exploration tools help understand factors influencing the range of invasive species. The models generate hypotheses about adaptive shifts in H. destructor.

In this paper, a new access method is proposed for current positions of moving objects on road networks in order to efficiently update their positions. In the existing index structures, the connectivity of edges is lost because the intersection points in which three or more edges are split. The proposed index structure preserves the network connectivity, which uses intersection oriented network model by not splitting intersection nodes that three or more edges meet for preserving the connectivity of adjacent road segments. The data node stores not only the positions of moving object but also the connectivity of networks.

The deterministic tree-based database indexing structures unintentionally retain information about previous operations records of the index itself and about the database users' past activities. Privacy is jeopardized when a user gains illegitimate access to an indexing structure and accesses this confidential information from the internal representation of the index. This paper is the first to explore the design of history-independent access methods for spatial and multidimensional databases and proposes the History-Independent R-tree (HIR-tree), an R-tree variant which does not reveal information about the sequence of insertions, deletions and updates that have been applied to it and only reveals the outcome of these historical operations. It differs from the traditional spatial access methods in that the HIR-tree has the additional property of hiding its modification history. This property is achieved through the use of randomization by the update algorithms.

The time spent in collecting current samples for decoder calibration and the computational burden brought by high-dimensional neural recordings remain two challenging problems in intracortical brain-machine interfaces (iBMIs). Decoder calibration optimization approaches have been proposed, and neuron selection methods have been used to reduce computational burden. However, few methods can solve both problems simultaneously. In this article, we present a symmetrical-uncertainty-based transfer learning (SUTL) method that combines transfer learning with feature selection. The proposed method uses symmetrical uncertainty to quantitatively measure three indices for feature selection: stationarity, importance and redundancy of the feature. By selecting the stationary features, the disparities between the historical data and current data can be diminished, and the historical data can be effectively used for decoder calibration, thereby reducing the demand for current data. After selecting the important and non-redundant features, only the channels corresponding to them need to work; thus, the computational burden is reduced. The proposed method was tested on neural data recorded from two rhesus macaques to decode the reaching position or grasping gesture. The results showed that the SUTL method diminished the disparities between the historical data and current data, while achieving superior decoding performance with the needs of only ten current samples each category, less than 10% the number of features and 30% the number of neural recording channels. Additionally, unlike most studies on iBMIs, feature selection was implemented instead of neuron selection, and the average decoding accuracy achieved by the former was 6.6% higher.

Environmental Risk Assessments (ERA) have been extensively carried out in 2014 by the Company (TOTAL) as part of the Technological Risk Assessment process. In that context it has been applied to nearshore Gabon pipelines and loading terminal located in the vicinity of Mandji Island and Cap Lopez. The approach used is consistent with the methodology already applied to far off-shore installations but in this case was applied for the first time to installations located at a few kilometers from the coast and using a validated set of hindcast current and wind data developed purposely for west coast Africa. The methodology applied takes into account the trajectory of potential oil spills and the probabilities that oil reaches any environmental resources. The tools used are 3D oil drift model (OSCAR) and exposure effect relations coming from the MIRA method developed in Norway. Four oil spill scenarios have been selected as representing most of oil spill cases requiring detailed environmental risk assessment on the basis of the technological risk assessment done on these installations. For each spill scenario, oil drift simulations were conducted in stochastic mode on the basis of 10 years historical wind and current data with 3km resolution. Quantities of oil and associated probabilities of presence were calculated for each cell of the model and comparison could be made with Argos drifter experiments in the case of one scenario. Shoreline habitats, turtles nesting beaches, seabirds, fisheries and mangroves have been selected as the key environmental resources to consider in this study. The results have been used to position the scenarios on the company risk matrix, assess environmental risk and address further needs on risk reduction measures. Results are discussed with some emphasis on the dominating role of current pattern in the area showing that environmental resources at risk are sometimes not the closest to the spill location. Validation aspects of the methodology related to current and Argos drifter experiment data are also discussed.

Healthcare information systems typically collect, store and manage various kinds of data such as illness details, clinical history, essential body parameters, health insurance plans, and other related data towards enabling data processing and analytics to arrive at better decision making with all the clarity and alacrity. To reduce the mortality rate due to heart diseases, it is essential to predict the presence of disease in its budding stage itself. Manual extraction of the useful knowledge from historical data is practically tedious and time-consuming. Machine learning (ML) algorithms are being used to detect and predict something useful out of both historical and current data. Despite the applicability of machine learning algorithms for prediction, the accuracy of prediction is significantly influenced by features used for prediction. Moreover, to meet the needs of evolving data sizes, suitable technologies for data storage also become essential. Based on these two aspects, a comparative analysis has been performed for feature selection using four filter methods, namely, correlation measure, information gain, gain ratio and relief. Further, a scalable architecture using Hadoop framework has been proposed to enable the machine learning algorithms to handle larger datasets while performing prediction task. The impact of the proposed architecture on the performance of machine learning algorithm has been evaluated with benchmark dataset and found to have improved scalability and accuracy.

Questionnaires are widely used in clinical research and mental health studies, but their responses are usually more subjective than clinical biomarkers. Researchers may use latent variable models such as the item response theory (IRT) model to characterize individual ability, but response instability and small sample sizes can lead to increased uncertainty in estimation. Historical data from similar questionnaires may help reduce variation, but they must be incorporated adaptively. The challenge is further compounded by the need to ensure computational efficiency in IRT models with a large number of parameters. In this work, we develop a Bayesian framework for adaptive borrowing in IRT models based on an approximated normalized power prior (NPP) that treats the borrowing weight as a random parameter. The proposed method makes NPP feasible for general IRT models and is coupled with a full Bayesian data augmentation strategy that enables joint estimation of ability and item parameters through an efficient Gibbs sampler. In simulations, the approach adaptively increases borrowing when historical and current data are compatible and automatically downweights conflicting information. Relative to analyzes without borrowing, the method reduces variance and mean squared error while maintaining coverage across a range of test lengths, item discrimination profiles, and historical-current concordance scenarios. We illustrate the method by integrating historical mental health surveys into current assessments, yielding more precise ability estimates with preserved calibration. An efficient implementation is provided in our updated package NPP available on the Comprehensive R Archive Network.

In order to benefit as much as possible from geographical information, users have expressed a strong need to integrate their data with other data. The need to integrate historical data is particularly expressed as it often contains invaluable information which is often unmapped or not represented in current maps or data. Unfortunately, easily accessible data integration treatments are not yet available for users. This paper aims to introduce a solution to solve this problem by explaining the building of a data integration web platform, called SEREDONA, a French acronym for “SErvice de REcalage de DONnees Anciennes” which can be translated as follows: “Web service platform for the adjustment of historical data”. The approach to build this platform, its functionalities and its architecture are fully described. In particular the architecture is based on OGC Web Services (Web Map Service, Web Feature Service and Web Processing Service), Open Source components and the principles of a service oriented architecture. Thus, it allows using the developed components in a more global spatial data infrastructure or separately in other contexts.

Estimating power system states accurately is crucial to the reliable operation of power grids. Traditional weighted least square (WLS) state estimation methods face the rising threat of cyber-attacks, such as false data injection attacks, which can pass the bad data detection process in WLS state estimation. In this paper, we propose a new detection method to detect false data injection attacks by tracking the dynamics of measurement variations. The Kullback-Leibler distance (KLD) is used to calculate the distance between two probability distributions derived from measurement variations. When false data are injected into the power systems, the probability distributions of the measurement variations will deviate from the historical data, thus leading to a larger KLD. The proposed method is tested on IEEE 14 bus system using load data from the New York independent system operator with different attack scenarios. We have also tested our method on false data injection attacks that replace current measurement data with historical measurement data. Test results show that the proposed approach can accurately detect most of the attacks.

Indexing transaction time databases

A tremendous amount of effort is spent by scientists to develop smart wearable devices that can monitor and track a patient's health status 24/7. Such smart wearable devices need to be equipped with intelligence in order to make decisions as humans do. Development of such an intelligent virtual medical assistant would be a better solution for monitoring physical and mental health. In addition, it can assist those who have movement and transportation issues in physical diagnosis and checkups. Older people and those who have movement-related diseases could benefit; such intelligent virtual medical assistants would be a boon either indoors or outdoors. A simple loT-enabled wearable device is an loT device with sensors to monitor health status on some basic parameters such as temperature, blood pressure, oxygen level, etc. However, in order to provide smart healthcare, intelligence needs to be embedded in these kinds of virtual assistants. This article discusses the application of Machine Intelligence (ML) algorithms to intelligent virtual medical assistants to provide improved solutions by tracking patients' historical data along with current data. Such assistants can then provide suggestions, notifications, and medical prescriptions for self improvement.

The article delves into the challenges of delivery time management in today's business landscape. The authors underscore the need for precise delivery time forecasts, a key factor in maintaining a competitive edge and meeting customer expectations. They outline various methods for estimating the time of a selected commodity based on historical data, and stress the necessity of modern tools that can adapt to the intricate web of factors influencing delivery times and facilitate swift responses to changes. The following article presents an innovative delivery time forecasting application that integrates advanced predictive algorithms with historical data, current data, and external factors affecting the delivery process. The application was developed to provide more accurate delivery time forecasts and optimize logistics processes. Through advanced technologies, it can consider even the most complex scenarios and changes, allowing companies to plan and manage their logistics operations more effectively.