Existing similarity search methods, often limited to scalar or vector data, struggle to identify complex patterns found in scientific datasets, such as 2D seismic events or 3D magnetic flux ropes. We introduce TensorSearch, a novel parallel similarity search paradigm designed to identify known patterns in high-dimensional tensors. By directly employing tensor representations, TensorSearch captures intricate pattern structures more effectively than traditional vector-based approaches. Furthermore, its parallel architecture optimizes cache and I/O operations, enabling efficient processing of large-scale scientific data. Our performance evaluations demonstrate that TensorSearch outperforms state-of-the-art vector-based systems like Milvus by up to 10x, and achieves up to a remarkable 55x advantage over custom solution developed in Matlab used by the domain scientists. In these tests, TensorSearch exhibits linear scalability, supporting up to 2240 CPU cores.

In contemporary scientific research, data acquisition and analysis platforms have grown increasingly complex, often spanning multiple facilities with diverse internal structures. Efficiently managing the interactions between job scheduling, resource allocation, and networking across these distributed systems requires a robust simulation framework. However, existing simulators fall short in capturing the detailed interactions necessary for comprehensive analysis of large-scale distributed environments. To address this gap, we introduce DISTRI, a versatile framework specifically designed for the development and testing of distributed multi-facility workflows. DISTRI allows for customizable facility configurations and includes built-in support for distributed, resilient scheduling and resource management, alongside detailed network simulation for data communication. Key features of DISTRI encompass inter- and intra-facility resource management, agent-based distributed scheduling, and extensive performance metrics logging for both resource and network management. By providing these essential tools, DISTRI enables thorough analysis and optimization, thereby advancing research in the resilience and efficiency of multi-facility systems.

Energy consumption in buildings accounts for a significant portion of the global energy use. Consequently, understanding building energy use is important. Data over the past decade show that the energy intensity (Joules/sqft) of commercial buildings has decreased. While some of the improvements (decrease in energy use) are easily measurable such as the use of more energy efficient lighting, impact of other modifications such as changes to the operation of the HVAC system or changes in the usage pattern of the building potentially due to external events are difficult to quantify. Simply comparing energy consumption prior and post change is not accurate as energy use is impacted by many factors including external weather conditions. In this paper, we present a case study to quantify the impact of external events on the energy consumption of a medium-sized office building. We adopt an approach based on counterfactual analysis. Towards this end, we first build two models based on Linear Regression and k-Nearest Neighbors to predict the daily energy use given different input features related to the weather. We determine the statistical features of the weather that are most predictive of energy use. We then use the models to determine a counterfactual baseline and thereby to accurately estimate the impact of the events. The results of the counterfactual analysis provide new insights on the impact of the events on energy consumption. The update to the building cooling system resulted in more energy savings than direct yearly comparison reveals. On the other hand, the tests of a MPC-based controller for the HVAC system saved less energy than determined by the direct yearly comparison. Finally, the results show that there no gains in terms of energy savings due to remote work during the COVID-19 pandemic. An increase in airflow setting in the HVAC system corroborates this finding and further validates the underlying model and the counterfactual analyses.

Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To address these shortcomings, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.

In a number of applications, data may be anonymized, obfuscated, or highly noisy. In such cases, it is difficult to use domain knowledge or low-dimensional visualizations to engineer the features for tasks such as machine learning, instead, we explore dimensionality reduction (DR) as a data-driven approach for engineering these low-dimensional representations. Through a careful examination of available feature selection and feature extraction techniques, we propose a new class named feature clustering. These new methods could utilize different forms of clustering to help evaluate the relative importance of features and take on properties different from the well-known DR algorithms. To evaluate these algorithms, we develop a parallel computing framework that optimizes their hyperparameters on a sample of application datasets. This framework harnesses the parallel computing power to examine a large number of parameter combinations and enables hyperparameter tuning and model tuning purely based on observed performance. This optimization framework provides mechanism for users to control computational cost and is able to examine many parameter choices in seconds. On a set of building energy data where the key features are known based on domain knowledge, the optimized DR algorithms indeed identify the expected main drivers of building electricity usage: outdoor temperature and solar radiance. This shows the automated optimization procedure is able to find known features. In terms of modeling accuracy, a distance correlation-based feature clustering method outperforms other DR algorithms including the well-known KPCA, LLE, and UMAP on two different tests.

Solid state storage systems have been very effectively used in small devices; however, their effectiveness for large systems such as supercomputers is not yet proven. Recently, for the first time, a new supercomputer is being deployed with an all-flash storage as its main file system. In this work, we report our preliminary study of the I/O performance on this supercomputer named Perlmutter. We are able to achieve 1.4 TB/s with the default file configuration on the system. This default configuration outperforms dozens of other choices tested, though the current observed performance is still pretty far from the theoretical peak performance of 5 TB/s.

Logistic regression has long been the gold standard for choice modeling in the transportation field. Despite the rising popularity of machine learning (ML), few is applied to predicting the household vehicle transactions. To address the research gap, this paper presents a first use case of ML application to predicting household vehicle transaction decisions by leveraging a newly processed national panel data set. Model performances are reported for four ML models and the traditional multinomial logit model (MNL). Instead of treating the gold standard and ML models as competitors, this paper tries to use ML tools to inform the MNL model building process. We find the two gradient boosting based methods, CatBoost and LightGBM, are the best performing ML models; and improving logistic models with SHAP interpretation tools can achieve similar performance levels to the best performing ML methods.

To understand behaviors of natural and man-made events, such as energy consumption of buildings, which accounts for 40% of energy uses in the US, we deploy automated monitoring devices to record periodic observations. However, such experimental and observation data often contains problems and irregularities that have to be cleaned up before analyses. Due to various conditions affecting sensor operations, the communication channels, recording steps, or the recording media, the recorded data might have missing values, errors, or anomalous values. An effective way to clean up these problems is to replace these missing values, errors and anomalous values with expected values, a process generally known as imputation. In this work, we survey commonly used missing value imputation techniques and compare their performance on a set of building monitoring data. To compare the different types of sensor measurements with widely varying characteristics, we use normalized root mean squared error (NRMSE) as the key metric for the effectiveness of the imputation methods. We additionally consider periodicity and run time when considering comparing methods. Through extensive testing, we find that for small gap sizes, up to 8 consecutive missing values, linear interpolation performs the best; for larger gaps stretching up to 48 consecutive missing values, K-nearest neighbors provides the most accurate imputations; for even larger gaps, more computational intensive methods, such as matrix factorization, achieve the smallest NRMSE. Additionally, we observe that these computationally intensive algorithms not only provide accurate imputations for large gaps, but are also more robust across all types of sensors.

Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. This information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard evaluation, and groundwater monitoring. However, for such processes to converge quickly, data segments with appropriate noise energy should be selected. Distributed Acoustic Sensing (DAS) is a novel sensing technique that enables acquisition of these data at very high spatial and temporal resolution for tens of kilometers. One major challenge when utilizing the DAS technology is the large volume of data that is produced, thereby presenting a significant Big Data challenge to find regions of useful energy. In this work, we present a highly scalable and efficient approach to process real, complex DAS data by integrating physics knowledge acquired during a data exploration phase followed by deep supervised learning to identify "useful" coherent surface waves generated by anthropogenic activity, a class of seismic waves that is abundant on these recordings and is useful for geophysical imaging. Data exploration and training were done on 130 Gigabytes (GB) of DAS measurements. Using parallel computing, we were able to do inference on an additional 170 GB of data (or the equivalent of 10 days’ worth of recordings) in less than 30 minutes. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors.

Wi-Fi has become the wireless networking standard that allows short- to medium-range device to connect without wires. For the last 20 year, the Wi-Fi technology has so pervasive that most devices in use today are mobile and connect to the internet through Wi-Fi. Unlike wired network, a wireless network lacks a clear boundary, which leads to significant Wi-Fi network security concerns, especially because the current security measures are prone to several types of intrusion. To address this problem, machine learning and deep learning methods have been successfully developed to identify network attacks. However, collecting data to develop models is expensive and raises privacy concerns. The goal of this paper is to evaluate a federated learning approach that would alleviate such privacy concerns. This initial work on intrusion detection is performed in a simulated environment. Once proven feasible, this process would allow edge devices to collaboratively update global anomaly detection models, without sharing sensitive training data. On a set of tests with the AWID intrusion detection data set, we show that our federated approach is effective in terms of classification accuracy, computation cost, as well as communication cost.

Scientific workflows are increasingly transferring large amounts of data between high performance computing (HPC) systems. Even though these HPC systems are connected via high-speed dedicated networks and use dedicated data transfer nodes (DTNs), it is still difficult to predict the data transfer throughput because of variations in data transfer protocols, host configurations, performance of file systems, and overlapping workloads. In order to provide reliable performance prediction for better resource management and job scheduling, we need models for predicting data transfer throughput under real-world conditions. In this paper, we explore different machine learning approaches for building data-driven models to improve performance and prediction of large-scale data transfer throughput. In addition to the variables already collected by the network monitoring system, we also develop heuristics to derive additional metrics for improving the prediction accuracy. We use the prediction results to identify the importance of different network parameters in predicting the throughput for large-scale data transfers. Through extensive tests, we identify key network parameters, discover interesting variations among different HPC sites, and show that we can predict throughput with high accuracy. We also analyze our models and results to provide recommendations for improving the performance of big data transfers.

The demands of increasingly large scientific application workflows lead to the need for more powerful supercomputers. As the scale of supercomputing systems have grown, the prediction of fault tolerance has become an increasingly critical area of study, since the prediction of system failures can improve performance by saving checkpoints in advance. We propose a real-time failure detection algorithm that adopts an event-based prediction model. The prediction model is a convolutional neural network that utilizes both traditional event attributes and additional spatio-temporal features. We present a case study using our proposed method with six years of reliability, availability, and serviceability event logs recorded by Mira, a Blue Gene/Q supercomputer at Argonne National Laboratory. In the case study, we have shown that our failure prediction model is not limited to predict the occurrence of failures in general. It is capable of accurately detecting specific types of critical failures such as coolant and power problems within reasonable lead time ranges. Our case study shows that the proposed method can achieve a F1score of 0.56 for general failures, 0.97 for coolant failures, and 0.86 for power failures.

Prediction of daily transportation mode use (car, public transit, or active travel) is a important task in transportation research. Unlike statistical models that impose a predetermined model structure, machine learning models are learned from the data, making them more flexible with higher prediction accuracy. However, prediction of mid-to long-term habitual modes still largely relies on traditional statistical analysis using small samples of cross-sectional data. Low interpretability of “black-box” machine learning models limits their usefulness for generating behavior insights needed for designing appropriate interventions. This paper, leveraging a set of unique longitudinal life course data, is the first use case to demonstrate machine learning methods applied for both predicting and interpreting regularly used travel modes. We combine sequence clustering and tree-based machine learning methods coupled with TreeExplainer to predict and interpret habitual travel modes using mid-to long-term predictors. Five life course clusters are derived to provide evaluation and interpretation contexts. This allows us to improve upon a recently developed TreeExplainer method to better distinguish predictor importance locally and globally; and predictor interactions across subpopulations within distinctive life history contexts. Our results demonstrate a promising step toward interpretable machine learning applications to mid-to long-term prediction of travel modes for transportation planning.

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.

Sensors typically record their measurements using more precision than the accuracy of the sensing techniques. Thus, experimental and observational data often contains noise that appears random and cannot be easily compressed. This noise increases storage requirement as well as computation time for analyses. In this work, we describe a line of research to develop data reduction techniques that preserve the key features while reduce the storage requirement. Our core observation is that the noise in such cases could be characterized by a small number of patterns based on statistical similarity. In earlier tests, this approach was shown to reduce the storage requirement by over 100-fold for one-dimensional sequences. In this work, we explore a set of different similarity measures for multidimensional sequences. During our tests with standard quality measures such as PSNR, we see that the new compression methods reduce the storage requirements over 100-fold while maintaining relatively low errors in peak signal-to-noise ratio. Thus, we believe that this is a new and effective way of constructing data reduction techniques.

A prime question for plasma physicists is how a fraction of charged particles is accelerated to very high energy.To answer this question, physicists simulate trillions of particles with detailed dynamics and analyze their trajectories. This process requires a range of data analysis tasks with high diversity. In this paper, we present a use case of formulating various analysis tasks on terabyte-scale particle data with a novel data analysis framework called ArrayUDF. The flexibility of ArrayUDF allows it to compose a wide range of particle data operations. We also present optimization strategies to avoid frequent global reduction and to take full advantage of the data locality. Tests show that our optimization methods could accelerate these particle data analysis operations by up to 1,600 times.

Scientific data analysis typically involves reading massive amounts of data that was generated by simulations, experiments, and observations. Performance of reading such large volumes of data from disk-based file systems is often poor because of the slow and mechanical components in the disks. Recent supercomputing systems are adding non-volatile storage layers in a hierarchy to handle the performance gap between fast main memory and slow disk-based storage. Software libraries for managing this hierarchy not only need efficient reading of data but also reduce user-involvement for cross-layer data movement. Furthermore, these libraries need to support array data access patterns into hierarchical storage management as scientific data is often organized in array-based data structures. Existing software typically manage individual storage layers requiring significant manual process in moving data among them. In this paper, we introduce a new array caching in hierarchical storage (ARCHIE) to accelerate array data analysis in a seamless fashion. ARCHIE evaluates array access patterns and prefetches data with array semantics between storage layers. Our evaluation shows that ARCHIE outperforms state-of-the-art file systems, i.e., Lustre and DataWarp, on a production supercomputing system by up to 5.8× in accessing data by scientific analysis applications.

Accurately predicting network traffic volume is beneficial for congestion control, improving routing, allocating network resources and network optimization. Traffic congestion happens when a network device is receiving more data packets than its processing capability. The number of retransmissions per flow, packet duplication and synthetic reordering can seriously degrade the overall TCP performance. An unsupervised/supervised technique to accurately identify TCP anomalies occurring during file transfers based on passive measurements of TCP traffic collected using Tstat is proposed. This method will be validated on real large datasets collected from several data transfer nodes. The preliminary results indicate that the percentage of TCP anomalies correlate well with the average throughput in any given time window.

The Linac Coherent Light Source (LCLS) located at SLAC National Accelerator Laboratory has been essential to over 1023 publications since 2009. The LCLS produces vast quantities of data - thousands of gigabytes per experiment. The data must be analyzed and stored at large data centers to be available to the world-wide user community. Due to the vast quantities of data flowing through the network, many abnormal data transfers remain unnoticed. This work focuses on identifying network failures that could slow down the data transfer process. This work aims to develop a diagnostic tool to detect when network transfers become anomalously slow. The tool uses an algorithm based on the hampel filter to detect poor performance and alert SLAC administrators to bottlenecks in each phase of the workflow. We will describe our experience of preparing the data and modifying the hampel filter to enhance its effectiveness. We found that applying a heuristic to the algorithm in conjunction with parsing the data along key features improved performance.

Scientists are increasingly turning to datacenter-scale computers to analyze massive arrays. Despite decades of database research that extols the virtues of declarative query processing, scientists still write, debug and parallelize imperative HPC programs even for the most mundane queries. This impedance mismatch is due to the cumbersome and costly data format conversions that are needed to use scientific data management tools, such as SciDB, in an HPC setting. Our goal is to make declarative array manipulations from SciDB interoperable with imperative, file-centric analyses from HDF5-based programs. This paper describes ArrayBridge, a bi-directional array view mechanism for the HDF5 file format, that allows scientists to use SciDB, TensorFlow and HDF5-based analysis code in the same file-centric pipeline without converting between file formats. In addition to fast querying over HDF5 array objects, ArrayBridge produces arrays in the HDF5 file format as easily as it can read from it. ArrayBridge also supports time travel queries from imperative codes through the unmodified HDF5 API, and automatically deduplicates between versions for space efficiency. Our performance evaluation in a large scientific computing facility shows that ArrayBridge exhibits statistically indistinguishable performance and I/O scalability to the native SciDB storage engine and is 3× faster than TileDB.

As applications continue to generate multi-dimensional data at exponentially increasing rates, fast analytics to extract meaningful results is becoming extremely important. The database community has developed array databases that alleviate this problem through a series of techniques. In-situ mechanisms provide direct access to raw data in the original format---without loading and partitioning. Parallel processing scales to the largest datasets. In-memory caching reduces latency when the same data are accessed across a workload of queries. However, we are not aware of any work on distributed caching of multi-dimensional raw arrays. In this paper, we introduce a distributed framework for cost-based caching of multi-dimensional arrays in native format. Given a set of files that contain portions of an array and an online query workload, the framework computes an effective caching plan in two stages. First, the plan identifies the cells to be cached locally from each of the input files by continuously refining an evolving R-tree index. In the second stage, an optimal assignment of cells to nodes that collocates dependent cells in order to minimize the overall data transfer is determined. We design cache eviction and placement heuristic algorithms that consider the historical query workload. A thorough experimental evaluation over two real datasets in three file formats confirms the superiority - by as much as two orders of magnitude - of the proposed framework over existing techniques in terms of cache overhead and workload execution time.

To ensure the reliability of power transformers, they are monitored for partial discharge (PD) events, which are symptoms of trans- former failure. Our goal is to classify PDs to gain an understanding of the location of failure. We develop a small set of features and a stacking ensemble that outperform larger feature sets and other models in both accuracy and variaTo ensure the reliability of power transformers, they are monitored for partial discharge (PD) events, which are symptoms of transformer failure. Our goal is to classify PDs to gain an understanding of the location of failure. We develop a small set of features and a stacking ensemble that outperform larger feature sets and other models in both accuracy and variance.nce.

The goal of this paper is to investigate the impact of missing values in categorical time series sequences on common data analysis tasks. Being able to more effectively identify patterns in socio-demographic longitudinal data is an important component in a number of social science settings. However, performing fundamental analytical operations, such as clustering for grouping these data based on similarity patterns, is challenging due to the categorical and multi-dimensional nature of the data, and their corruption by missing and inconsistent values. To study these data quality issues, we employ longitudinal sequence data representations, a similarity measure designed for categorical and longitudinal data, together with state-of-the art clustering methodologies reliant on hierarchical algorithms. The key to quantifying the similarity and difference among data records is a distance metric. Given the categorical nature of our data, we employ an “edit” type distance using Optimal Matching (OM). Because each data record has multiple variables of different types, we investigate the impact of mixing these variables in a single similarity measure. Between variables with binary values and those with multiple nominal values, we find that the ability to overcome missing data problems is harder in the nominal domain versus the binary domain. Additionally, artificial clusters introduced by the alignment of leading missing values can be resolved by tuning the missing value substitution cost parameter.

The goal of our study is to analyze massive high-frequency streaming sensor data to accurately locate the source of partial discharges (PD) in transformers. The PD signal is collected by ultra-high frequency sensors at a resolution of 0.4 ns per record resulting in a data streaming rate of 12 GB/s. A voltage threshold is applied to the data stream to extract 400 ns signal samples. We develop a voltage threshold method based on the Savitzky-Golay filter for signal arrival timing, and localize the PD with arrival time differences using Finite-Difference Time-Domain (FDTD) simulation. The Savitzky-Golay filter is able to preserve features better than other methods, resulting in improved signal-to-noise ratios and more accurate signal timing. FDTD accounts for the travel path of signals inside the transformer, allowing for more precise PD localization. Our resulting method localizes PDs more accurately than existing methods, particularly in high noise cases.

We propose a new class of lossy compression based on locally exchangeable measure that captures the distribution of repeating data blocks while preserving unique patterns. The technique has been demonstrated to reduce data volume by more than 100-fold on power grid monitoring data where a large number of data blocks can be characterized as following stationary probability distributions. To capture data with more diverse patterns, we propose two techniques to transform non-stationary time series into locally stationary blocks. We also propose a strategy to work with values in bounded ranges such as phase angles of alternating current. These new ideas are incorporated into a software package named IDEALEM. In experiments, IDEALEM reduces non-stationary data volume up to 100-fold. Compared with the state-of-the-art lossy compression methods such as SZ, IDEALEM can produce more compact output overall.

Science applications are producing an ever-increasing volume of multi-dimensional data that are mainly processed with distributed array databases. These raw arrays are ``cooked'' into derived data products using complex pipelines that are time-consuming. As a result, derived data products are released infrequently and become stale soon thereafter. In this paper, we introduce materialized array views as a database construct for scientific data products. We model the ``cooking'' process as incremental view maintenance with batch updates and give a three-stage heuristic that finds effective update plans. Moreover, the heuristic repartitions the array and the view continuously based on a window of past updates as a side-effect of view maintenance without overhead. We design an analytical cost model for integrating materialized array views in queries. A thorough experimental evaluation confirms that the proposed techniques are able to incrementally maintain a real astronomical data product in a production environment.

We propose a new class of lossy compression based on locally exchangeable measure that captures the distribution of repeating data blocks while preserving unique patterns. The technique has been demonstrated to reduce data volume by more than 100-fold on power grid monitoring data where a large number of data blocks can be characterized as following stationary probability distributions. To capture data with more diverse patterns, we propose two techniques to transform non-stationary time series into locally stationary blocks. We also propose a strategy to work with values in bounded ranges such as phase angles of alternating current. These new ideas are incorporated into a software package named IDEALEM. In experiments, IDEALEM reduces non-stationary data volume up to 100-fold. Compared with the state-of-the-art lossy compression methods such as SZ, IDEALEM can produce more compact output overall.

Runtime data sharing across applications is of great importance for avoiding high I/O overhead for scientific data analytics. Sharing data on a staging space running on a set of dedicated compute nodes is faster than writing data to a slow disk-based parallel file system (PFS) and then reading it back for post-processing. Originally, the staging space has been purely based on main memory (DRAM), and thus was several orders of magnitude faster than the PFS approach. However, storing all the data produced by large-scale simulations on DRAM is impractical. Moving data from memory to SSD-based burst buffers is a potential approach to address this issue. However, SSDs are about one order of magnitude slower than DRAM. To optimize data access performance over the staging space, methods such as prefetching data from SSDs according to detected spatial access patterns and distributing data across the network topology have been explored. Although these methods work well for uniform mesh data, which they were designed for, they are not well suited for adaptive mesh refinement (AMR) data. Two major issues must be addressed before constructing such a memory hierarchy and topology-aware runtime AMR data sharing framework: (1) spatial access pattern detection and prefetching for AMR data; (2) AMR data distribution across the network topology at runtime. We propose a framework that addresses these challenges and demonstrate its effectiveness with extensive experiments on AMR data. Our results show the framework's spatial access pattern detection and prefetching methods demonstrate about 26% performance improvement for client analytical processes. Moreover, the framework's topology-aware data placement can improve overall data access performance by up to 18%.

Scientific applications are generating an ever-increasing volume of multi-dimensional data that are largely processed inside distributed array databases and frameworks. Similarity join is a fundamental operation across scientific workloads that requires complex processing over an unbounded number of pairs of multi-dimensional points. In this paper, we introduce a novel distributed similarity join operator for multi-dimensional arrays. Unlike immediate extensions to array join and relational similarity join, the proposed operator minimizes the overall data transfer and network congestion while providing load-balancing, without completely repartitioning and replicating the input arrays. We define formally array similarity join and present the design, optimization strategies, and evaluation of the first array similarity join operator.

Applications such as scientific simulations and power grid monitoring are generating so much data quickly that compression is essential to reduce storage requirement or transmission capacity. To achieve better compression, one is often willing to discard some repeated information. These lossy compression methods are primarily designed to minimize the Euclidean distance between the original data and the compressed data. But this measure of distance severely limits either reconstruction quality or compression performance. We propose a new class of compression method by redefining the distance measure with a statistical concept known as exchangeability. This approach reduces the storage requirement and captures essential features, while reducing the storage requirement. In this paper, we report our design and implementation of such a compression method named IDEALEM. To demonstrate its effectiveness, we apply it on a set of power grid monitoring data, and show that it can reduce the volume of data much more than the best known compression method while maintaining the quality of the compressed data. In these tests, IDEALEM captures extraordinary events in the data, while its compression ratios can far exceed 100.

In the era of data-intensive scientific discovery, data analysis is critical for scientists to identify essential information from the mountains of data generated by large-scale simulations or experiments. A generic operation in scientific data analysis is to combine information from multiple data sets, which are stored in heterogeneous ile formats. This operation is typically known as a Join in database management field. Currently, a join operation involving multiple data sets in different file formats is time-consuming because of the need to prepare data (i.e., to convert data into a uniform format or to ingest into a database) and to run the join algorithms. Furthermore, data processing languages, such as SQL (Structured Query Language), can not easily express typical scientific analysis tasks such as interpolation. In this paper, we propose three techniques to address these challenges: a two-level data model to process data from different file formats without converting to a uniform format, a data organization structure known as Multi-Dimensional Binning (MDBin), and a join processing algorithm known as Spatially Clustered Join (SCJoin). Together, these techniques allow scientific data files to be used for query processing with less I/O cost and fast query response time without the extra cost to perform ile format conversion and data ingestion. Evaluation of our proposed techniques in joining and interpolating data sets generated by a plasma physics simulation studying space weather phenomenon showed up to 8X improvement over FastQuery. Querying with our solution outperforms SciDB, a popular array data management system for scientific data, by 43X-143X. We also demonstrate that our methods scale to 64K CPU cores in analyzing 32TB data on a large-scale supercomputing system.

Scientific data is often shared among researchers and even reorganized by colleagues or third-party users. Thus, it is essential to provide an adequate degree of access control for such shared data to preserve a desired level of security requirements. In this work, we develop an essential, lightweight access control model for secure data services in a limited distributed setting such as an HPC cluster. In particular, we consider SDS (the Scientific Data Services framework) as a use case system, which is a framework offering performance-optimized data access, reorganization, and analysis. We outline the requirements and challenges for access control for effective data services, and develop an authorization service model based on the defined requirements. We also present an initial prototyping model.

Scientific experiments and large-scale simulations produce massive amounts of data. Many of these scientific datasets are arrays, and are stored in file formats such as HDF5 and NetCDF. Although scientific data management systems, such as SciDB, are designed to manipulate arrays, there are challenges in integrating these systems into existing analysis workflows. Major barriers include the expensive task of preparing and loading data before querying, and converting the final results to a format that is understood by the existing post-processing and visualization tools. As a consequence, integrating a data management system into an existing scientific data analysis workflow is time-consuming and requires extensive user involvement. In this paper, we present the design of a new scientific data analysis system that efficiently processes queries directly over data stored in the HDF5 file format. This design choice eliminates the tedious and error-prone data loading process, and makes the query results readily available to the next processing steps of the analysis workflow. Our design leverages the increasing main memory capacities found in supercomputers through bitmap indexing and in-memory query execution. In addition, query processing over the HDF5 data format can be effortlessly parallelized to utilize the ample concurrency available in large-scale supercomputers and modern parallel file systems. We evaluate the performance of our system on a large supercomputing system and experiment with both a synthetic dataset and a real cosmology observation dataset. Our system frequently outperforms the relational database system that the cosmology team currently uses, and is more than 10X faster than Hive when processing data in parallel. Overall, by eliminating the data loading step, our query processing system is more effective in supporting in situ scientific analysis workflows.

Electricity is a vital part of our daily life; therefore it is important to avoid irregularities such as the California Electricity Crisis of 2000 and 2001. In this work, we seek to predict anomalies using advanced machine learning algorithms, more specifically a Change Point Detection (CPD) algorithm on the electricity prices during the California Electricity Crisis. Such algorithms are effective, but computationally expensive when applied on a large amount of data. To address this challenge, we accelerate the Gaussian Process (GP) for 1-dimensional time series data. Since GP is at the core of many statistical learning techniques, this improvement could benefit many algorithms. In the specific Change Point Detection algorithm used in this study, we reduce the overall computational complexity from O(n5) to O(n2), where the amountized cost of solving a GP projet is O(1). Our efficient algorithm makes it possible to compute the Change Points using the hourly price data during the California Electricity Crisis. By comparing the detected Change Points with known events, we show that the Change Point Detection algorithm is indeed effective in detecting signals preceding major events.

FastQuery is a parallel indexing and querying system we developed for accelerating analysis and visualization of scientific data. We have applied it to a wide variety of HPC applications and demonstrated its capability and scalability using a petascale trillion-particle simulation in our previous work. Yet, through our experience, we found that performance of reading and writing data with FastQuery, like many other HPC applications, could be significantly affected by various tunable parameters throughout the parallel I/O stack. In this paper, we describe our success in tuning the performance of FastQuery on a Lustre parallel file system. We study and analyze the impact of parameters and tunable settings at file system, MPI-IO library, and HDF5 library levels of the I/O stack. We demonstrate that a combined optimization strategy is able to improve performance and I/O bandwidth of FastQuery significantly. In our tests with a trillion-particle dataset, the time to index the dataset reduced by more than one half.

Gene context analysis determines the function of genes by examining the conservation of chromosomal gene clusters and co-occurrence functional profiles across genomes. This is based on the observation that functionally related genes are often collocated on chromosomes as part of so called "gene cassettes", and relies on the identification of such cassettes across a statistically significant and phylogenetically diverse collection of genomes. Gene context analysis is an important part of a genomic data management system such as the Integrated Microbial Genomes (IMG) system, which has one of the largest public genome collections. As of January 2013, IMG contains 3.3 million gene cassettes across 8,000 genomes. A gene context analysis in IMG performs many millions of comparisons among the cassettes and their functions. Using a traditional relational database management system, these cassettes and their functional characteristics are represented by a correlation table of more than 2 billion rows along with a dozen auxiliary tables. This correlation table requires 16.5 hours to build and a typical query requires 5 to 10 minutes to answer.

Due to their low access latency, high read speed, and power-efficient operation, flash memory storage devices are rapidly emerging as an attractive alternative to traditional magnetic storage devices. However, tests show that the most efficient indexing methods are not able to take full advantage of flash memory storage devices. In this paper, we present a set of multi-level bitmap indexes that can effectively utilize flash storage devices. These indexing methods use coarsely binned indexes to answer queries approximately, and then use finely binned indexes to refine the answers. Our new methods read significantly lower volumes of data at the expense of an increased disk access count, thus taking full advantage of the improved read speed and low access latency of flash devices. To demonstrate the advantage of these new indexes, we measure their performance on a number of storage systems using a standard data warehousing benchmark called the Set Query Benchmark. We observe that multilevel strategies on flash drives are up to 3 times faster than traditional indexing strategies on magnetic disk drives.

Bitmap indexes are known as the most effective indexing methods for range queries on append-only data, and many different bitmap indexes have been proposed in the research literature. However, only two of the simplest ones are used in commercial products. To better understand the benefits offered by the more sophisticated variations, we conduct an analytical comparison of well-known bitmap indexes, most of which are in the class of multi-component bitmap indexes. Our analysis is the first to fully incorporate the effects of compression on their performance. We produce closed-form formulas for both the index sizes and the query processing costs for the worst cases. One surprising finding is that the two simple indexes are in fact the best among multi-component indexes. Additionally, we investigate a number of novel variations in a class of multi-level indexes, and find that they answer queries faster than the best of multi-component indexes. More specifically, some two-level indexes are predicted by analyses and verified with experiments to be 5 to 10 times faster than well-known indexes. Furthermore, these two-level indexes have the optimal computational complexity for answering queries.

We present a new class of adaptive algorithms that use compressed bitmap indexes to speed up evaluation of the range join query in relational databases. We determine the best strategy to process a join query based on a fast sub-linear time computation of the join selectivity (the ratio of the number of tuples in the result to the total number of possible tuples). In addition, we use compressed bitmaps to represent the join output compactly: the space requirement for storing the tuples representing the join of two relations is asymptotically bounded by min(h; n.cb), where h is the number of tuple pairs in the result relation, n is the number of tuples in the smaller of the two relations, and cb is the cardinality of the larger column being joined. We present a theoretical analysis of our algorithms, as well as experimental results on large-scale synthetic and real data sets. Our implementations are efficient, and consistently outperform well-known approaches for a range of join selectivity factors. For instance, our count-only algorithm is up to three orders of magnitude faster than the sort-merge approach, and our best bitmap index-based algorithm is 1.2x-80x faster than the sort-merge algorithm, for various query instances. We achieve these speedups by exploiting several inherent performance advantages of compressed bitmap indexes for join processing: an implicit partitioning of the attributes, space-efficiency, and tolerance of high-cardinality relations.

Data management systems for "big science" often have tight memory and disk space constraints. In this paper, we introduce adaptive bitmap indexes, which conform to both space limits while dynamically adapting to the query load and offering excellent performance. So that adaptive bitmap indexes can use optimal bin boundaries, we show how to improve the scalability of optimal binning algorithms so that they can be used with real- world workloads. As the removal of false positives is the largest component of lookup time for a small-footprint bitmap index, we propose a novel way to materialize and drop auxiliary projection indexes, to eliminate the need to visit the data store to check for false positives. Our experiments with real-world data and queries show that adaptive bitmap indexes offer approximately 100- 300% performance improvement (compared to standard binned bitmap indexes) at a cost of 5 MB of dedicated memory, under disk storage constraints that would cripple other indexes.

Historically, bitmap indexing has provided an important database capability to accelerate queries. However, only a few database systems have implemented these indexes because of the difficulties of modifying fundamental assumptions in the low- level design of a database system and in the expectations of customers, both of which have developed in an environment that does not support bitmap indexes. Another problem that arises, and one that may more easily be addressed by a research article, is that there is no definitive design for bitmap indexes; bitmap index designs in Oracle, Sybase IQ, Vertica and MODEL 204 are idiosyncratic, and some of them were designed for older machine architectures. To investigate an efficient design on modern processors, this paper provides details of the Set Query benchmark and a comparison of two research implementations of bitmap indexes. One, called RIDBit, uses the N-ary storage model to organize table rows, and implements a strategy that gracefully switches between the well-known B-tree RID-list structure and a bitmap structure. The other, called FastBit is based on vertical organization of the table data, where all columns are individually stored. It implements a compressed bitmap index, with a linear organization of the bitmaps to optimize disk accesses. Through this comparison, we evaluate the pros and cons of various design choices. Our analysis adds a number of subtleties to the conventional indexing wisdom commonly quoted in the database community.

Applications that query data streams in order to identify trends, patterns, or anomalies can often benefit from comparing the live stream data with archived historical stream data. However, searching this historical data in real time has been considered so far to be prohibitively expensive. One of the main bottlenecks is the update costs of the indices over the archived data. In this paper, we address this problem by using our highly-efficient bitmap indexing technology (called FastBit) and demonstrate that the index update operations are sufficiently efficient for this bottleneck to be removed. We describe our prototype system based on the TelegraphCQ streaming query processor and the FastBit bitmap index. We present a detailed performance evaluation of our system using a complex query workload for analyzing real network traffic data. The combined system uses TelegraphCQ to analyze streams of traffic information and FastBit to correlate current behaviors with historical trends. We demonstrate that our system can simultaneously analyze (1) live streams with high data rates and (2) a large repository of historical stream data.

Large scale scientific data is often stored in scientific data formats such as FITS, netCDF and HDF. These storage formats are of particular interest to the scientific user community since they provide multi-dimensional storage and retrieval. However, one of the drawbacks of these storage formats is that they do not support semantic indexing which is important for interactive data analysis where scientists look for features of interests such as "Find all supernova explosions where energy > 10^5 and temperature > 10^6". In this paper we present a novel approach called HDF5- FastQuery to accelerate the data access of large HDF5 files by introducing multi-dimensional semantic indexing. Our implementation leverages an efficient indexing technology called bitmap indexing that has been widely used in the database community. Bitmap indices are especially well suited for interactive exploration of large-scale readonly data. Storing the bitmap indices into the HDF5 file has the following advantages: a) Significant performance speedup of accessing subsets of multi-dimensional data and b) portability of the indices across multiple computer platforms. We will present an API that simplifies the execution of queries on HDF5 files for general scientific applications and data analysis. The design is flexible enough to accommodate the use of arbitrary indexing technology for semantic range queries. We will also provide a detailed performance analysis of HDF5-FastQuery for both synthetic and scientific data. The results demonstrate that our proposed approach for multi-dimensional queries is up to a factor of 2 faster than HDF5.

Bitmap indices have been widely used in scientific applications and commercial systems for processing complex,multi-dimensional queries where traditional tree-based indices would not work efficiently. A common approach for reducing the size of a bitmap index for high cardinality attributes is to group ranges of values of an attribute into bins and then build a bitmap for each bin rather than a bitmap for each value of the attribute. Binning reduces storage costs,however, results of queries based on bins often require additional filtering for discarding it false positives, i.e., records in the result that do not satisfy the query constraints. This additional filtering,also known as ''candidate checking,'' requires access to the base data on disk and involves significant I/O costs. This paper studies strategies for minimizing the I/O costs for ''candidate checking'' for multi-dimensional queries. This is done by determining the number of bins allocated for each dimension and then placing bin boundaries in optimal locations. Our algorithms use knowledge of data distribution and query workload. We derive several analytical results concerning optimal bin allocation for a probabilistic query model. Our experimental evaluation with real life data shows an average I/O cost improvement of at least a factor of 10 for multi-dimensional queries on datasets from two different applications. Our experiments also indicate that the speedup increases with the number of query dimensions.

Bitmap indices are efficient for answering queries on low-cardinality attributes. In this article, we present a new compression scheme called Word-Aligned Hybrid (WAH) code that makes compressed bitmap indices efficient even for high-cardinality attributes. We further prove that the new compressed bitmap index, like the best variants of the B-tree index, is optimal for one-dimensional range queries. More specifically, the time required to answer a one-dimensional range query is a linear function of the number of hits. This strongly supports the well-known observation that compressed bitmap indices are efficient for multidimensional range queries because results of one-dimensional range queries computed with bitmap indices can be easily combined to answer multidimensional range queries. Our timing measurements on range queries not only confirm the linear relationship between the query response time and the number of hits, but also demonstrate that WAH compressed indices answer queries faster than the commonly used indices including projection indices, B-tree indices, and other compressed bitmap indices.

In this paper, we propose a new strategy for optimizing the placement of bin boundaries to minimize the cost of query evaluation using bitmap indices with binning. For attributes with a large number of distinct values, often the most efficient index scheme is a bitmap index with binning. However, this type of index may not be able to fully resolve some user queries. To fully resolve these queries, one has to access parts of the original data to check whether certain candidate records actually satisfy the specified conditions. We call this procedure the candidate check, which usually dominates the total query processing time. Given a set of user queries, we seek to minimize the total time required to an-swer the queries by optimally placing the bin boundaries. We show that our dynamic programming based algorithm can efficiently determine the bin boundaries. We verify our analysis with some real user queries from the Sloan Digital Sky Survey. For queries that require significant amount of time to perform candidate check, using our optimal bin boundaries reduces the candidate check time by a factor of 2 and the total query processing time by 40%.

Many scientific applications generate large spatio-temporal datasets. A common way of exploring these datasets is to identify and track regions of interest. Usually these regions are defined as contiguous sets of points whose attributes satisfy some user defined conditions, e.g. high temperature regions in a combustion simulation. At each time step, the regions of interest may be identified by first searching for all points that satisfy the conditions and then grouping the points into connected regions. To speed up this process, the searching step may use a tree-based indexing scheme, such as a KD-tree or an Octree. However, these indices are efficient only if the searches are limited to one or a small number of selected attributes. Scientific datasets often contain hundreds of attributes and scientists frequently study these attributes in complex combinations, e.g. finding regions of high temperature and low pressure. Bitmap indexing is an efficient method for searching on multiple criteria simultaneously. We apply a bitmap compression scheme to reduce the size of the indices. In addition, we show that the compressed bitmaps can be used efficiently to perform the region growing and the region tracking operations. Analyses show that our approach scales well and our tests on two datasets from simulation of the autoignition process show impressive performance.

As data warehousing applications grow in size, existing data organizations and access strategies, such as relational tables and B-tree indexes, are becoming increasingly ine#ective. The two primary reasons for this are that these datasets involve many attributes and the queries on the data usually involve conditions on small subsets of the attributes. Two strategies are known to address these di#culties well, namely vertical partitioning and bitmap indexes. In this paper, we summarize our experience of implementing a number of bitmap index schemes on vertically partitioned data tables. One important observation is that simply scanning the vertically partitioned data tables is often more e#cient than using B-tree based indexes to answer ad hoc range queries on static datasets. For these range queries, compressed bitmap indexes are in most cases more e#cient than scanning vertically partitioned tables. We evaluate the performance of two di#erent compression schemes for bitmap indexes stored is various ways. Using the compression scheme called WordAligned Hybrid Code (WAH) to store the bitmaps in plain files shows the best overall performance for bitmap indexes. Tests indicate that our bitmap index strategy based on WAH is not only e#cient for attributes of low cardinality, say, < 100, but also for high-cardinality attributes with 200,000 or more distinct values.

We study the effects of compression on bitmap indexes. The main operations on the bitmaps during query processing are bitwise logical operations. Using the general purpose compression schemes the logical operations on the compressed bitmaps are much slower than on the uncompressed bitmaps. Specialized compression schemes, like the byte-aligned bitmap code (BBC), are usually faster in performing logical operations than the general purpose schemes, but in many cases they are still orders of magnitude slower than the uncompressed scheme. To make the compressed bitmap indexes operate more efficiently, we designed a CPU-friendly scheme which we refer to as the word-aligned hybrid code (WAH). Tests on both synthetic and real application data show that the new scheme significantly outperforms well-known compression schemes at a modest increase in storage space. Compared to BBC, WAH performs logical operations about 12 times faster and uses only 60% more space. Compared to the uncompressed scheme, in most test cases WAH is faster while still using less space. We further verified with additional tests that the improvement in logical operation speed translates to similar improvement in query processing speed.

We present a comparison of two new word-aligned schemes with some schemes for compressing bitmap indexes, including the well-known byte-aligned bitmap code (BBC). On both synthetic data and real application data, the new word-aligned schemes use only 50% more space, but perform logical operations on compressed data 12 times faster than BBC. The new schemes achieve this performance advantage by guaranteeing that during logical operations every machine instruction performs useful work on words rather than on bytes or bits as in BBC.