Roadside LiDAR (Light Detection and Ranging) sensors promise safer and faster traffic management and vehicular operations. However, occlusion and small view angles are significant challenges to widespread use of roadside LiDARs. We consider fusing data from multiple LiDARs at a traffic intersection to better estimate traffic parameters than one can estimate from a single LiDAR. The key challenge is to calibrate multiple LiDARs both in time and space. The problem is more complex when heterogeneous sensors differ in resolution and are positioned arbitrarily on a traffic intersection.

Subsurface earthquakes and explosions generate seismic wavefields that are recorded as time-domain signals on sensor networks around the world. To compute key characteristics such as the magnitude of these seismic events, analysts must detect and select the cleanest indicators of seismic phase amplitudes and periods in noisy signals. Existing automated systems designed to pick seismic phase amplitudes and periods require frequent adjustments by human analysts, which becomes a nuisance when the volume of data to process grows large. To address this problem, we have developed a neural network model that accurately replicates the performance of a human analyst 80% of the time and shows potential for decreasing analyst burden by over 40%. We have performed multiple tests on the model and report on its performance compared to existing deep learning techniques.

In the Bitcoin blockchain, an entity (e.g., a gambling service) may control multiple distinct address clusters. Links (i.e., trust relationships) between these disjoint address clusters can be established when one cluster is abandoned, and a new one is formed shortly thereafter. To link the clusters across time, we have developed a deep neural network model that exploits these synchronous actions derived from unlabeled data in a self-supervised manner. This model assesses whether two clusters exhibit synchronous temporal signatures indicative of a shared entity ownership.

Seismic monitoring systems sift through seismograms in real-time, searching for target events, such as underground explosions. In this monitoring system, a burst of aftershocks (minor earthquakes occur after a major earthquake over days or even years) can be a source of confounding signals. Such a burst of aftershock signals can overload the human analysts of the monitoring system. To alleviate this burden at the onset of a sequence of events (e.g., aftershocks), a human analyst can label the first few of these events and start an online classifier to filter out subsequent aftershock events. We propose an online few-shot classification model FewSig for time series data for the above use case. The framework of FewSig consists of a selective model to identify the high-confidence positive events which are used for updating the models and a general classifier to label the remaining events. Our specific technique uses a %two-level decision tree selective model based on sliding DTW distance and a general classifier model based on distance metric learning with Neighborhood Component Analysis (NCA). The algorithm demonstrates surprising robustness when tested on univariate datasets from the UEA/UCR archive. Furthermore, we show two real-world earthquake events where the FewSig reduces the human effort in monitoring applications by filtering out the aftershock events.

Roadside LiDAR (Light Detection and Ranging) sensors are recently being explored for intelligent transportation systems aiming at safer and faster traffic management and vehicular operations. A key challenge in such systems is to efficiently transfer massive point-cloud data from the roadside LiDAR devices to the edge connected through a 5G network for real-time processing. In this paper, we consider the problem of compressing roadside (i.e. static) LiDAR data in real-time that provides a unique condition unexplored by current methods. Existing point-cloud compression methods assume moving LiDARs (that are mounted on vehicles) and do not exploit spatial consistency across frames over time.

Time series anomaly detection remains one of the most active areas of research in data mining. In spite of the dozens of creative solutions proposed for this problem, recent empirical evidence suggests that time series discords, a relatively simple twenty-year old distance-based technique, remains among the state-of-art techniques. While there are many algorithms for computing the time series discords, they all have limitations. First, they are limited to the batch case, whereas the online case is more actionable. Second, these algorithms exhibit poor scalability beyond tens of thousands of datapoints. In this work we introduce DAMP, a novel algorithm that addresses both these issues. DAMP computes exact left-discords on fast arriving streams, at up to 300,000 Hz using a commodity desktop. This allows us to find time series discords in datasets with trillions of datapoints for the first time. We will demonstrate the utility of our algorithm with the most ambitious set of time series anomaly detection experiments ever conducted.

The depth of a seismic event is an essential feature to discriminate natural earthquakes from events induced or created by humans. However, estimating the depth of a seismic event with a sparse set of seismic stations is a daunting task, and there is no globally usable method. This paper focuses on developing a machine learning model to accurately estimate the depth of arbitrary seismic events directly from seismograms. Our proposed deep learning architecture is not-so-deep compared to commonly found models in the literature for related tasks, consisting of two loosely connected levels of neural networks, associated with the seismic stations at the higher level and the individual channels of a station at the lower level. Thus, the model has significant advantages, including a reduced number of parameters for tuning and better interpretability to geophysicists. We evaluate our solution on seismic data collected from the SCEDC (Southern California Earthquake Data Center) catalog for regional events in California. The model can learn waveform features specific to a set of stations, while it struggles to generalize to completely novel sets of event sources and stations. In a simplified setting of separating shallow events from deep ones, the model achieved an 86.5% F1-score using the Southern California stations.

Monitoring systems have hundreds or thousands of distributed sensors gathering and transmitting real-time streaming data. The early detection of events in these systems, such as an earthquake in a seismic monitoring system, is the base for essential tasks as warning generations. To detect such events is usual to compute pairwise correlation across the disparate signals generated by the sensors. Since the data sources (e.g., sensors) are spatially separated, it is essential to consider the lagged correlation between the signals. Besides, many applications require to process a specific band of frequencies depending on the event’s type, demanding a pre-processing step of filtering before computing correlations. Due to the high speed of data generation and a large number of sensors in these systems, the operations of filtering and lagged cross-correlation need to be efficient to provide real-time responses without data losses. This article proposes a technique named FilCorr that efficiently computes both operations in one single step. We achieve an order of magnitude speedup by maintaining frequency transforms over sliding windows. Our method is exact, devoid of sensitive parameters, and easily parallelizable. Besides our algorithm, we also provide a publicly available real-time system named Seisviz that employs FilCorr in its core mechanism for monitoring a seismometer network. We demonstrate that our technique is suitable for several monitoring applications as seismic signal monitoring, motion monitoring, and neural activity monitoring.

Online social media enables mass-level, transparent, and democratized discussion on numerous socio-political issues. Due to such openness, these platforms often endure manipulation and misinformation - leading to negative impacts. To prevent such harmful activities, platform moderators employ countermeasures to safeguard against actors violating their rules. However, the correlation between publicly outlined policies and employed action is less clear to general people.

Post-specific diffusion network elucidates the who-saw-from-whom paths of a post on social media. A diffusion network for a specific post can reveal trustworthy and/or incentivized connections among users. Unfortunately, such a network is not observable from available information from social media platforms; hence an inference mechanism is needed.

This paper introduces a new pattern mining task that considers aligning or joining a set of time series based on an arbitrary number of subsequences (i.e., patterns) with arbitrary lengths. Joining multiple time series along common patterns can be pivotal in clustering and summarizing large time series datasets. An exact algorithm to join hundreds of time series based on multi-length patterns is impractical due to the high computational costs. This paper proposes a fast algorithm named MultiPAL to join multiple time series at interactive speed to summarize large time series datasets. The algorithm exploits Matrix Profiles of the individual time series to enable a greedy search over possible joins. The algorithm is orders of magnitude faster than the exact solution and can utilize hundreds of Matrix Profiles. We evaluate our algorithm for sequential mining on data from various real-world domains, including power management and bioacoustics monitoring.

Seismic phase identification classifies the type of seismic wave received at a station based on the waveform (i.e., time series) recorded by a seismometer. Automated phase identification is an integrated component of large scale seismic monitoring applications, including earthquake warning systems and underground explosion monitoring. Accurate, fast, and fine-grained phase identification is instrumental for earthquake location estimation, understanding Earth's crustal and mantle structure for predictive modeling, etc. However, existing operational systems utilize multiple nearby stations for precise identification, which delays response time with added complexity and manual interventions. Moreover, single-station systems mostly perform coarse phase identification. In this paper, we revisit the seismic phase classification as an integrated part of a seismic processing pipeline. We develop a machine-learned model FASER, that takes input from a signal detector and produces phase types as output for a signal associator. The model is a combination of convolutional and long short-term memory networks. Our method identifies finer wave types, including crustal and mantle phases. We conduct comprehensive experiments on real datasets to show that FASER outperforms existing baselines. We evaluate FASER holding out sources and stations across the world to demonstrate consistent performance for novel sources and stations.

Multivariate time series (MTS) are collected for different variables in studying scientific phenomena or monitoring system health where each time series records the values of one variable for a time period. Among the different variables, it is common that only a few variables contribute significantly to a specific phenomenon. Furthermore, the variables contributing significantly to different phenomena are often different. We denote the different variables that contribute to the occurrences of different phenomena as Phenomenon-specific Variables (PVs). In this paper, we formulate a novel problem of identifying significant PVs from MTS datasets. To analyze MTS data, feature extraction techniques have been extensively studied. However, most of them identify important global features for one dataset and do not utilize the temporal order of time series. To solve the newly introduced problem, we propose a solution framework, CNNmts-X, which is a new variant of the Convolutional Neural Networks (CNN) and can embed other feature extraction techniques (as X). Furthermore, we design a CNNmts-LR method that implements a new feature identification approach (LR) as Xin the CNNmts-X framework. The LR method leverages both Linear Discriminant Analysis (LDA) and Random Forest (RF). Our extensive experiments on five real datasets show that the CNNmts-LR method has exhibited much better performance than several other baseline methods. Using 30 percent of the PVs discovered from the CNNmts-LR, classifications can achieve better or similar performance than using all the variables.

The discovery of time series motifs has emerged as one of the most useful primitives in time series data mining. Researchers have shown its utility for exploratory data mining, summarization, visualization, segmentation, classification, clustering, and rule discovery. Although there has been more than a decade of extensive research, there is still no technique to allow the discovery of time series motifs in the presence of missing data, despite the well-documented ubiquity of missing data in scientific, industrial, and medical datasets. In this work, we introduce a technique for motif discovery in the presence of missing data. We formally prove that our method is admissible, producing no false negatives. We also show that our method can “piggy-back” off the fastest known motif discovery method with a small constant factor time/space overhead. We will demonstrate our approach on diverse datasets with varying amounts of missing data.

Changes in data distribution of streaming data (i.e., concept drifts), constitute a central issue in online data mining. The main reason is that these changes are responsible for outdating stream learning models, reducing their predictive performance over time. A common approach adopted by real-time adaptive systems to deal with concept drifts is to employ detectors that indicate the best time for updates. However, an unrealistic assumption of most detectors is that the labels become available immediately after data arrives. In this paper, we introduce an unsupervised and model-independent concept drift detector suitable for high-speed and high-dimensional data streams in realistic scenarios with the scarcity of labels. We propose a straightforward two-dimensional representation of the data aiming faster processing for detection. We develop a simple adaptive drift detector on this visual representation that is efficient for fast streams with thousands of features and is accurate as existing costly methods that perform various statistical tests. Our method achieves better performance measured by execution time and accuracy in classification problems for different types of drifts, including abrupt, oscillating, and incremental. Experimental evaluation demonstrates the versatility of the method in several domains, including astronomy, entomology, public health, political science, and medical science.

An essential task on streaming time series data is to compute pairwise correlation across disparate signal sources to identify significant events. In many monitoring applications, such as geospatial monitoring, motion monitoring and critical infrastructure monitoring, correlation is observed at various frequency bands and temporal lags. In this paper, we consider computing filtered and lagged correlation on streaming time series data, which is challenging because the computation must be “in-sync” with the incoming stream for any detected events to be useful. We propose a technique to compute filtered and lagged correlation on streaming data efficiently by merging two individual operations: filtering and cross-correlations. We achieve an order of magnitude speed-up by maintaining frequency transforms over sliding windows. Our method is exact, devoid of sensitive parameters, and easily parallelizable. We demonstrate our technique in a seismic signal monitoring application.

Social bots have been around for over a decade since 2008. Social bots are capable of swaying political opinion, spreading false information, and recruiting for terrorist organizations. Social bots use various sophisticated techniques by adopting emotions, sympathy following, synchronous deletions, and profile molting. There are several approaches proposed in the literature for detection, exploration, and measuring social bots. We provide a comprehensive overview of the existing work from data mining and machine learning perspective, discuss relative strengths and weaknesses of various methods, make recommendations for researchers and practitioners, and propose novel directions for future research in taming the social bots. The tutorial also discusses pitfalls in collecting and sharing data on social bots.

Unlike semi-supervised clustering, classification and rule discovery; semi-supervised motif discovery is a surprisingly unexplored area in data mining. Semi-supervised Motif Discovery finds hidden patterns in long time series when a few arbitrarily known patterns are given. A naive approach is to exploit the known patterns and perform similarity search within a radius of the patterns. However, this method would find only similar shapes and would be limited in discovering new shapes. In contrast, traditional unsupervised motif discovery algorithms detect new shapes, while missing some patterns because the given information is not utilized. We propose a semi-supervised motif discovery algorithm that forms a nearest neighbor graph to identify chains of nearest neighbors from the given events. We demonstrate that the chains are likely to identify hidden patterns in the data. We have applied the method to find novel events in several geoscientific datasets more accurately than existing methods.

Real-valued data sequences are often affected by structured noise in addition to random noise. For example, in pressure transient analysis (PTA), semi-log derivatives of log-log diagnostic plots show such contamination of structured noise; especially under multiphase flow condition. In PTA data, structured noise refers to the response to some physical phenomena which is not originated at the reservoir, such as fluid segregation in wellbore or pressure leak due to a brief opening of a valve. Such noisy responses commonly appear to mix up with flow regimes, hindering further reservoir flow analysis. In this paper, we use the Singular Spectrum Analysis (SSA) to decompose PTA data into additive components; subsequently we use the eigenvalues associated with the decomposed components to identify the components that contain most of the structured noise information. We develop a semisupervised process that requires minimal expert supervision in tuning the solitary parameter of our algorithm using only one pressure buildup scenario. An empirical evaluation using real pressure data from oil and gas wells shows that our approach can detect a multitude of structured noise with 74.25% accuracy.

Bots (i.e. automated accounts) involve in social campaigns typically for two obvious reasons: to inorganically sway public opinion and to build social capital exploiting the organic popularity of social campaigns. In the process, bots interact with each other and engage in human activities (e.g. likes, retweets, and following).

The Posting schedule reveals characteristic patterns of users on social media. Motivated by this knowledge, several researchers have modeled posting schedules and argued that deviation from the model indicates bot or spammer characteristics. It is true that circadian rhythms induce regularity in human posting behavior; however, in this paper, we show that this regularity is an individual trait and insufficient to develop a generic model. More surprisingly, we show that bots are more structured in their posting behaviors compared to humans by using a Convolutional Neural Network (CNN). More precisely, we demonstrate using Class Activation Maps that bots contain less entropy than humans. Thus, we conclude that bots are more amenable to generic models than humans. We evaluate the hypothesis on more than 32 million posts from 12 thousand Twitter users with 97% accuracy.

Matching a dictionary of patterns (i.e. subsequences) against a streaming time series to identify occurrences is one of the primary components of real-time monitoring systems such as earthquake monitoring, power consumption monitoring, and patient monitoring. These domains critically depend on timely alarms immediately after events (i.e. earthquake, fire, seizure, etc.) start. Until now, the problem has been solved independently by smart pruning, efficient approximation, and pattern indexing without bounding the delay between pattern occurrence and detection time. Moreover, complexity of the dictionary matching problem is quickly growing with larger dictionary sizes, faster data streams, and stricter delay requirements; pushing existing pattern matching systems to their limits. In this paper, we describe a robust distributed matching system, called DisPatch (Distributed Pattern Matching), that matches a pattern with a guaranteed maximum delay after the pattern appears in the stream. We develop and evaluate a novel distribution strategy and integrate state-of-the-art algorithmic optimization techniques to horizontally scale to a high data rate and a large dictionary size. We show three use cases of DisPatch in seismic, patient and power consumption monitoring.

In an online review system, a user writes a review with the intention of helping fellow consumers (i.e. the readers) to make informed decisions. However, product owners often provide incentives (e.g. coupons, bonus points, referral rewards) to the writers, motivating the writing of biased reviews. These biased reviews, while beneficial for both writers and product owners, pollute the review space and destroy readers' trust significantly. In this paper, we analyze incentivized reviews in the Google Play store and identify a wide range of anomalous review types such as copying, spamming, advertising, and hidden-beneficiary reviews. We also find an increasing trend in the number of apps being targeted by abusers, which, if continued, will render review systems as crowd advertising platforms rather than an unbiased source of helpful information.

We propose BotWalk, a near-real time adaptive Twitter exploration algorithm to identify bots exhibiting novel behavior. Due to suspension pressure, Twitter bots are constantly changing their behavior to evade detection. Traditional supervised approaches to bot detection are non-adaptive and thus cannot identify novel bot behaviors. We therefore devise an unsupervised approach, which allows us to identify bots as they evolve. We characterize users with a behavioral feature vector which consists of (well-studied in isolation) metadata-, content-, temporal-, and network-based features. We identify a random bot from our seed bank, populated initially by previously-labeled bots, gather this user's followers' features from Twitter in real time, and employ an unsupervised ensemble anomaly detection method in the multi-dimensional behavioral space. These potential bots are folded into the seed bank and the process is then repeated, with the new seeds' features allowing us to adaptively identify novel bot behavior. BotWalk allows for the identification of on average 6,000 potential bots a day. Our method allowed us to detect 7,995 previously undiscovered bots from a sample of 15 seed bots with a precision of 90%.

How can we automatically clean and curate online reviews to better mine them for knowledge discovery? Typical online reviews are full of noise and abnormalities, hindering semantic analysis and leading to a poor customer experience. Abnormalities include non-standard characters, unstructured punctuation, different/multiple languages, and misspelled words. Worse still, people will leave “junk” text, which is either completely nonsensical, spam, or fraudulent. In this paper, we describe three types of noisy and abnormal reviews, discuss methods to detect and filter them, and, finally, show the effectiveness of our cleaning process by improving the overall distributional characteristics of review datasets.

Modern engineering incorporates smart technologies in all aspects of our lives. Smart technologies are generating terabytes of log messages every day to report their status. It is crucial to analyze these log messages and present usable information (e.g. patterns) to administrators, so that they can manage and monitor these technologies. Patterns minimally represent large groups of log messages and enable the administrators to do further analysis, such as anomaly detection and event prediction. Although patterns exist commonly in automated log messages, recognizing them in massive set of log messages from heterogeneous sources without any prior information is a significant undertaking. We propose a method, named LogMine, that extracts high quality patterns for a given set of log messages. Our method is fast, memory efficient, accurate, and scalable. LogMine is implemented in map-reduce framework for distributed platforms to process millions of log messages in seconds. LogMine is a robust method that works for heterogeneous log messages generated in a wide variety of systems. Our method exploits algorithmic techniques to minimize the computational overhead based on the fact that log messages are always automatically generated. We evaluate the performance of LogMine on massive sets of log messages generated in industrial applications. LogMine has successfully generated patterns which are as good as the patterns generated by exact and unscalable method, while achieving a 500× speedup. Finally, we describe three applications of the patterns generated by LogMine in monitoring large scale industrial systems.

We develop a warped correlation finder to identify correlated user accounts in social media websites such as Twitter. The key observation is that humans cannot be highly synchronous for a long duration, thus, highly synchronous user accounts are most likely bots. Existing bot detection methods are mostly supervised, which requires a large amount of labeled data to train, and do not consider cross-user features. In contrast, our bot detection system works on activity correlation without requiring labeled data. We develop a novel lag-sensitive hashing technique to cluster user accounts into correlated sets in near real-time. Our method, named DeBot, detects thousands of bots per day with a 94% precision and generates reports online everyday. In September 2016, DeBot has accumulated about 544,868 unique bots in the previous one year. We compare our detection technique with per-user techniques and with Twitter's suspension system. We observe that some bots can avoid Twitter's suspension mechanism and remain active for months, and, more alarmingly, we show that DeBot detects bots at a rate higher than the rate Twitter is suspending them.

Dynamic Time Warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, in its original formulation DTW is extremely inefficient in comparing long sparse time series, containing mostly zeros and some unevenly spaced non-zero observations. Original DTW distance does not take advantage of this sparsity, leading to redundant calculations and a prohibitively large computational cost for long time series. We derive a new time warping similarity measure (AWarp) for sparse time series that works on the run-length encoded representation of sparse time series. The complexity of AWarp is quadratic on the number of observations as opposed to the range of time of the time series. Therefore, AWarp can be several orders of magnitude faster than DTW on sparse time series. AWarp is exact for binary-valued time series and a close approximation of the original DTW distance for any-valued series. We discuss useful variants of AWarp: bounded (both upper and lower), constrained, and multidimensional. We show applications of AWarp to three data mining tasks including clustering, classification, and outlier detection, which are otherwise not feasible using classic DTW, while producing equivalent results. Potential areas of application include bot detection, human activity classification, and unusual review pattern mining.

The all-pairs-similarity-search (or similarity join) problem has been extensively studied for text and a handful of other datatypes. However, surprisingly little progress has been made on similarity joins for time series subsequences. The lack of progress probably stems from the daunting nature of the problem. For even modest sized datasets the obvious nested-loop algorithm can take months, and the typical speed-up techniques in this domain (i.e., indexing, lower-bounding, triangular-inequality pruning and early abandoning) at best produce one or two orders of magnitude speedup. In this work we introduce a novel scalable algorithm for time series subsequence all-pairs-similarity-search. For exceptionally large datasets, the algorithm can be trivially cast as an anytime algorithm and produce high-quality approximate solutions in reasonable time. The exact similarity join algorithm computes the answer to the time series motif and time series discord problem as a side-effect, and our algorithm incidentally provides the fastest known algorithm for both these extensively-studied problems. We demonstrate the utility of our ideas for two time series data mining problems, including motif discovery and novelty discovery.

Time series motifs have been in the literature for about fifteen years, but have only recently begun to receive significant attention in the research community. This is perhaps due to the growing realization that they implicitly offer solutions to a host of time series problems, including rule discovery, anomaly detection, density estimation, semantic segmentation, etc. Recent work has improved the scalability to the point where exact motifs can be computed on datasets with up to a million data points in tenable time. However, in some domains, for example seismology, there is an insatiable need to address even larger datasets. In this work we show that a combination of a novel algorithm and a high-performance GPU allows us to significantly improve the scalability of motif discovery. We demonstrate the scalability of our ideas by finding the full set of exact motifs on a dataset with one hundred million subsequences, by far the largest dataset ever mined for time series motifs. Furthermore, we demonstrate that our algorithm can produce actionable insights in seismology and other domains.

Dynamic Time Warping (DTW) is a distance measure that compares two time series after optimally aligning them. DTW is being used for decades in thousands of academic and industrial projects despite the very expensive computational complexity, O(n2). These applications include data mining, image processing, signal processing, robotics and computer graphics among many others. In spite of all this research effort, there are many myths and misunderstanding about DTW in the literature, for example "it is too slow to be useful" or "the warping window size does not matter much." In this tutorial, we correct these misunderstandings and we summarize the research efforts in optimizing both the efficiency and effectiveness of both the basic DTW algorithm, and of the higher-level algorithms that exploit DTW such as similarity search, clustering and classification. We will discuss variants of DTW such as constrained DTW, multidimensional DTW and asynchronous DTW, and optimization techniques such as lower bounding, early abandoning, run-length encoding, bounded approximation and hardware optimization. We will discuss a multitude of application areas including physiological monitoring, social media mining, activity recognition and animal sound processing. The optimization techniques are generalizable to other domains on various data types and problems.

Time series shapelets are small subsequences that maximally differentiate classes of time series. Since the inception of shapelets, researchers have used shapelets for various data domains including anthropology and health care, and in the process suggested many efficient techniques for shapelet discovery. However, multi-dimensional time series data poses unique challenges to shapelet discovery that are yet to be solved. We show that an ensemble of shapelet-based decision trees on individual dimensions works better than shapelets defined over multiple dimensions. Generating a shapelet ensemble for multidimensional time series is computationally expensive. Most of the existing techniques prune shapelet candidates for speed. In this paper, we propose a novel technique for shapelet discovery that evaluates remaining candidates efficiently. Our algorithm uses a multi-length approximate index for time series data to efficiently find the nearest neighbors of the candidate shapelets. We employ a simple skipping technique for additional candidate pruning and a voting based technique to improve accuracy while retaining interpretability. Not only do we find a significant speed increase, our techniques enable us to efficiently discover shapelets on datasets with multi-dimensional and long time series such as hours of brain activity recordings. We demonstrate our approach on a biomedical dataset and find significant differences between patients with schizophrenia and healthy controls.

Online reviews on products and services can be very useful for customers, but they need to be protected from manipulation. So far, most studies have focused on analyzing online reviews from a single hosting site. How could one leverage information from multiple review hosting sites? This is the key question in our work. In response, we develop a systematic methodology to merge, compare, and evaluate reviews from multiple hosting sites. We focus on hotel reviews and use more than 15 million reviews from more than 3.5 million users spanning three prominent travel sites. Our work consists of three thrusts: (a) we develop novel features capable of identifying cross-site discrepancies effectively, (b) we conduct arguably the first extensive study of cross-site variations using real data, and develop a hotel identity-matching method with 93% accuracy, (c) we introduce the TrueView score, as a proof of concept that cross-site analysis can better inform the end user. Our results show that: (1) we detect 7 times more suspicious hotels by using multiple sites compared to using the three sites in isolation, and (2) we find that 20% of all hotels appearing in all three sites seem to have low trustworthiness score. Our work is an early effort that explores the advantages and the challenges in using multiple reviewing sites towards more informed decision making.

Phonemes are the smallest units of sound produced by a human being. Automatic classification of phonemes is a well-researched topic in linguistics due to its potential for robust speech recognition. With the recent advancement of phonetic segmentation algorithms, it is now possible to generate datasets of millions of phonemes automatically. Phoneme classification on such datasets is a challenging data mining task because of the large number of classes (over a hundred) and complexities of the existing methods. In this paper, we introduce the phoneme classification problem as a data mining task. We propose a dual-domain (time and frequency) hierarchical classification algorithm. Our method uses a Dynamic Time Warping (DTW) based classifier in the top layers and time-frequency features in the lower layer. We cross-validate our method on phonemes from three online dictionaries and achieved up to 35% improvement in classification compared to existing techniques. We provide case studies on classifying accented phonemes and speaker invariant phoneme classification.

We consider the problem of joining two long time series based on their most correlated segments. Two time series can be joined at any locations and for arbitrary length. Such join locations and length provide useful knowledge about the synchrony of the two time series and have applications in many domains including environmental monitoring, patient monitoring and power monitoring. However, join on correlation is a computationally expensive task, specially when the time series are large. The naive algorithm requires O (n4) computation where n is the length of the time series. We propose an algorithm, named Jocor, that uses two algorithmic techniques to tackle the complexity. First, the algorithm reuses the computation by caching sufficient statistics and second, the algorithm prunes unnecessary correlation computation by admissible heuristics. The algorithm runs orders of magnitude faster than the naive algorithm and enables us to join long time series as well as many small time series. We propose a variant of Jocor for fast approximation and an extension to a GPU-based parallel method to bring down the running-time to interactive level for analytics applications. We show three independent uses of time series join on correlation which are made possible by our algorithm.

Recent advances in high throughput data collection and storage technologies have led to a dramatic increase in the availability of high-resolution time series data sets in various domains. These time series reflect the dynamics of the underlying physical processes in these domains. Detecting changes in a time series over time or changes in the relationships among the time series in a data set containing multiple contemporaneous time series can be useful to detect changes in these physical processes. Contextual events detection algorithms detect changes in the relationships between multiple related time series. In this work, we introduce a new type of contextual events, called group level contextual change events. In contrast to individual contextual change events that reflect the change in behavior of one target time series against a context, group level events reflect the change in behavior of a target group of time series relative to a context group of time series. We propose an online framework to detect two types of group level contextual change events: (i) group formation (i.e., detecting when a set of multiple unrelated timeseries or groups of time series with little prior relationship in their behavior forms a new group of related time series) and (ii) group disbanding (i.e., detecting when one stable set of related time series disbands into two or more subgroups with little relationship in their behavior). We demonstrate this framework using 2 real world datasets and show that the framework detects group level contextual change events that can be explained by plausible causes.

Time series motifs are repeated patterns in long and noisy time series. Motifs are typically used to understand the dynamics of the source because repeated patterns with high similarity evidentially rule out the presence of noise. Recently, time series motifs have also been used for clustering, summarization, rule discovery and compression as features. For all such purposes, many high quality motifs of various lengths are desirable and thus, originates the problem of enumerating motifs for a wide range of lengths. Existing algorithms find motifs for a given length. A trivial way to enumerate motifs is to run one of the algorithms for the whole range of lengths. However, such parameter sweep is computationally infeasible for large real datasets. In this paper, we describe an exact algorithm, called MOEN, to enumerate motifs. The algorithm is an order of magnitude faster than the naive algorithm. The algorithm frees us from re-discovering the same motif at different lengths and tuning multiple data-dependent parameters. The speedup comes from using a novel bound on the similarity function across lengths and the algorithm uses only linear space unlike other motif discovery algorithms. We describe three case studies in entomology and activity recognition where MOEN enumerates several high quality motifs.

Queries on the web can easily result in a large number of results. Result Diversification, a process by which the query provides the k most diverse set of matches, enables the user to better understand/explore such large results. Computing the diverse subset from a large set of results needs a massive number of pair-wise distance computations as well as finding the subset that maximizes the total pair-wise distance, which is NP-hard and requires efficient approximate algorithm.

Time series clustering has become an increasingly important research topic over the past decade. Most existing methods for time series clustering rely on distances calculated from the entire raw data using the Euclidean distance or Dynamic Time Warping distance as the distance measure. However, the presence of significant noise, dropouts, or extraneous data can greatly limit the accuracy of clustering in this domain. Moreover, for most real world problems, we cannot expect objects from the same class to be equal in length. As a consequence, most work on time series clustering only considers the clustering of individual time series "behaviors," e.g., individual heart beats or individual gait cycles, and contrives the time series in some way to make them all equal in length. However, contriving the data in such a way is often a harder problem than the clustering itself. In this work, we show that by using only some local patterns and deliberately ignoring the rest of the data, we can mitigate the above problems and cluster time series of different lengths, i.e., cluster one heartbeat with multiple heartbeats. To achieve this we exploit and extend a recently introduced concept in time series data mining called shapelets. Unlike existing work, our work demonstrates for the first time the unintuitive fact that shapelets can be learned from unlabeled time series. We show, with extensive empirical evaluation in diverse domains, that our method is more accurate than existing methods. Moreover, in addition to accurate clustering results, we show that our work also has the potential to give insights into the domains to which it is applied.

Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. The difficulty of scaling search to large datasets largely explains why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine truly massive time series for the first time. We demonstrate the following extremely unintuitive fact; in large datasets we can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms. We demonstrate our work on the largest set of time series experiments ever attempted. In particular, the largest dataset we consider is larger than the combined size of all of the time series datasets considered in all data mining papers ever published. We show that our ideas allow us to solve higher-level time series data mining problem such as motif discovery and clustering at scales that would otherwise be untenable. In addition to mining massive datasets, we will show that our ideas also have implications for real-time monitoring of data streams, allowing us to handle much faster arrival rates and/or use cheaper and lower powered devices than are currently possible.

The recent digitization of more than twenty million books has been led by initiatives from countries wishing to preserve their cultural heritage and by commercial endeavors, such as the Google Print Library Project. Within a few years a significant fraction of the world's books will be online. For millions of intact books and tens of millions of loose pages, the provenance of the manuscripts may be in doubt or completely unknown, thus denying historians an understanding of the context of the content. In some cases it may be possible for human experts to regain the provenance by examining linguistic, cultural and/or stylistic clues. However, such experts are rare and this investigation is clearly a time-consuming process. One technique used by experts to establish provenance is the examination of the ornate initial letters appearing in the questioned manuscript. By comparing the initial letters in the manuscript to annotated initial letters whose origin is known, the provenance can be determined. In this work we show for the first time that we can reproduce this ability with a computer algorithm. We leverage off a recently introduced technique to measure texture similarity and show that it can recognize initial letters with an accuracy that rivals or exceeds human performance. A brute force implementation of this measure would require several years to process a single large book; however, we introduce a novel lower bound that allows us to process the books in minutes.

Many animals produce long sequences of vocalizations best described as “songs.” In some animals, such as crickets and frogs, these songs are relatively simple and repetitive chirps or trills. However, animals as diverse as whales, bats, birds and even the humble mice considered here produce intricate and complex songs. These songs are worthy of study in their own right. For example, the study of bird songs has helped to cast light on various questions in the nature vs. nurture debate. However, there is a particular reason why the study of mice songs can benefit mankind. The house mouse (Mus musculus) has long been an important model organism in biology and medicine, and it is by far the most commonly used genetically altered laboratory mammal to address human diseases. While there has been significant recent efforts to analyze mice songs, advances in sensor technology have created a situation where our ability to collect data far outstrips our ability to analyze it. In this work we argue that the time is ripe for archives of mice songs to fall into the purview of data mining. We show a novel technique for mining mice vocalizations directly in the visual (spectrogram) space that practitioners currently use. Working in this space allows us to bring an arsenal of data mining tools to bear on this important domain, including similarity search, classification, motif discovery and contrast set mining.

Time series shapelets are small, local patterns in a time series that are highly predictive of a class and are thus very useful features for building classifiers and for certain visualization and summarization tasks. While shapelets were introduced only recently, they have already seen significant adoption and extension in the community. Despite their immense potential as a data mining primitive, there are two important limitations of shapelets. First, their expressiveness is limited to simple binary presence/absence questions. Second, even though shapelets are computed offline, the time taken to compute them is significant. In this work, we address the latter problem by introducing a novel algorithm that finds shapelets in less time than current methods by an order of magnitude. Our algorithm is based on intelligent caching and reuse of computations, and the admissible pruning of the search space. Because our algorithm is so fast, it creates an opportunity to consider more expressive shapelet queries. In particular, we show for the first time an augmented shapelet representation that distinguishes the data based on conjunctions or disjunctions of shapelets. We call our novel representation Logical-Shapelets. We demonstrate the efficiency of our approach on the classic benchmark datasets used for these problems, and show several case studies where logical shapelets significantly outperform the original shapelet representation and other time series classification techniques. We demonstrate the utility of our ideas in domains as diverse as gesture recognition, robotics, and biometrics.

Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding time series motifs exactly in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory . In this work, we leverage off previous work on pivot-based indexing to introduce a disk-aware algorithm to find time series motifs exactly in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before.

Many time series data mining problems require subsequence similarity search as a subroutine. Dozens of similarity/distance measures have been proposed in the last decade and there is increasing evidence that Dynamic Time Warping (DTW) is the best measure across a wide range of domains. Given DTW's usefulness and ubiquity, there has been a large community-wide effort to mitigate its relative lethargy. Proposed speedup techniques include early abandoning strategies, lower-bound based pruning, indexing and embedding. In this work we argue that we are now close to exhausting all possible speedup from software, and that we must turn to hardware-based solutions. With this motivation, we investigate both GPU (Graphics Processing Unit) and FPGA (Field Programmable Gate Array) based acceleration of subsequence similarity search under the DTW measure. As we shall show, our novel algorithms allow GPUs to achieve two orders of magnitude speedup and FPGAs to produce four orders of magnitude speedup. We conduct detailed case studies on the classification of astronomical observations and demonstrate that our ideas allow us to tackle problems that would be untenable otherwise.

The detection of repeated subsequences, time series motifs, is a problem which has been shown to have great utility for several higher-level data mining algorithms, including classification, clustering, segmentation, forecasting, and rule discovery. In recent years there has been significant research effort spent on efficiently discovering these motifs in static offline databases. However, for many domains, the inherent streaming nature of time series demands online discovery and maintenance of time series motifs. In this paper, we develop the first online motif discovery algorithm which monitors and maintains motifs exactly in real time over the most recent history of a stream. Our algorithm has a worst-case update time which is linear to the window size and is extendible to maintain more complex pattern structures. In contrast, the current offline algorithms either need significant update time or require very costly pre-processing steps which online algorithms simply cannot afford.

We consider the problem of computing all-pair correlations in a warehouse containing a large number (e.g., tens of thousands) of time-series (or, signals). The problem arises in automatic discovery of patterns and anomalies in data intensive applications such as data center management, environmental monitoring, and scientific experiments. However, with existing techniques, solving the problem for a large stream warehouse is extremely expensive, due to the problem's inherent quadratic I/O and CPU complexities.

Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding exact time series motifs in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory. In this work, we describe for the first time a disk-aware algorithm to find exact time series motifs in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before.

Time series motifs are pairs of individual time series, or subsequences of a longer time series, which are very similar to each other. As with their discrete analogues in computational biology, this similarity hints at structure which has been conserved for some reason and may therefore be of interest. Since the formalism of time series motifs in 2002, dozens of researchers have used them for diverse applications in many different domains. Because the obvious algorithm for computing motifs is quadratic in the number of items, more than a dozen approximate algorithms to discover motifs have been proposed in the literature. In this work, for the first time, we show a tractable exact algorithm to find time series motifs. As we shall show through extensive experiments, our algorithm is up to three orders of magnitude faster than brute-force search in large datasets. We further show that our algorithm is fast enough to be used as a subroutine in higher level data mining algorithms for anytime classification, near-duplicate detection and summarization, and we consider detailed case studies in domains as diverse as electroencephalograph interpretation and entomological telemetry data mining.