With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysisis a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.

Outlier Analysis

Update anywhere-anytime-anyway transactional replication has unstable behavior as the workload scales up: a ten-fold increase in nodes and traffic gives a thousand fold increase in deadlocks or reconciliations. Master copy replication (primary copy) schemes reduce this problem. A simple analytic model demonstrates these results. A new two-tier replication algorithm is proposed that allows mobile (disconnected) applications to propose tentative update transactions that are later applied to a master copy. Commutative update transactions avoid the instability of other replication schemes.

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The dangers of replication and a solution

The authors examine potential relationships among categories of personal information, beliefs about direct marketing, situational characteristics, specific privacy concerns, and consumers’ direct marketing shopping habits. Furthermore, the authors offer an assessment of the trade-offs consumers are willing to make when they exchange personal information for shopping benefits. The findings indicate that public policy and self-regulatory efforts to alleviate consumer privacy concerns should provide consumers with more control over the initial gathering and subsequent dissemination of personal information. Such efforts must also consider the type of information sought, because consumer concern and willingness to provide marketers with personal data vary dramatically by information type.

Privacy Concerns and Consumer Willingness to Provide Personal Information

In the statistics community, outlier detection for time series data has been studied for decades. Recently, with advances in hardware and software technology, there has been a large body of work on temporal outlier detection from a computational perspective within the computer science community. In particular, advances in hardware technology have enabled the availability of various forms of temporal data collection mechanisms, and advances in software technology have enabled a variety of data management mechanisms. This has fueled the growth of different kinds of data sets such as data streams, spatio-temporal data, distributed streams, temporal networks, and time series data, generated by a multitude of applications. There arises a need for an organized and detailed study of the work done in the area of outlier detection with respect to such temporal datasets. In this survey, we provide a comprehensive and structured overview of a large set of interesting outlier definitions for various forms of temporal data, novel techniques, and application scenarios in which specific definitions and techniques have been widely used.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Gupta%20-%20Outlier%20Detection%20for%20Temporal%20Data%20-%202014.pdf

Outlier Detection for Temporal Data: A Survey

Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.

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Map-matching for low-sampling-rate GPS trajectories

A database system is, by one definition [4], "a system whose overall purpose is to maintain information and to make that information available on demand." Database systems have evolved as part of a response to the desire to separate concerns. That is, the "application" should be responsible for manipulation of information and for its presentation to a user, and the "database system" should perform application-independent data management operations. A true separation, however, is difficult to achieve in practice. Often, the internal data structures and physical placement of data within the database system are optimized to achieve efficient performance for those queries known to be critical for the target application. The data schema itself may also reflect performance concerns. For particularly demanding applications, such as information filtering, database management may be completely customized to meet the needs of the application and even embedded within the application itself. The Datacycle TM architecture [2, 10] is a radical approach to database management. It attempts to achieve a full separation of concerns between the needs of many applications and the database management system supporting those needs, even for applications with unusually high demands for flexibility in access to data and for performance. In the Datacycle architecture, the contents of the entire database are broadcast cyclically over high-bandwidth communication facilities to multiple very large-scale integration (VLSI) data filters that perform complex associative search operations in parallel. The cyclic broadcasting and hardware filtering of information allow a different set of technology trade-offs affecting system architecture, functionality, and performance. The result is a unique combination of functionality and performance to support complex information management applications: • As in most database systems based on the relational model [3], the interface between the

The Datacycle architecture

The evolutionary trend toward a database-driven public communications network has motivated research into database architectures capable of executing thousands of transactions per second. In this paper we introduce the Datacycle architecture, an attempt to exploit the enormous transmission bandwidth of optical systems to permit the implementation of high throughput multiprocessor database systems. The architecture has the potential for unlimited query throughput, simplified data management, rapid execution of complex queries, and efficient concurrency control. We describe the logical operation of the architecture and discuss implementation issues in the context of a prototype system currently under construction.

The datacycle architecture for very high throughput database systems

The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for identifying abnormal moving activities. Indeed, various aspects of abnormality of moving patterns have recently been exploited, such as wrong direction and wandering. However, there is no recognized way of combining different aspects into an unified evolving abnormality score which has the ability to capture the evolving nature of abnormal moving trajectories. To that end, in this paper, we provide an evolving trajectory outlier detection method, named TOP-EYE, which continuously computes the outlying score for each trajectory in an accumulating way. Specifically, in TOP-EYE, we introduce a decay function to mitigate the influence of the past trajectories on the evolving outlying score, which is defined based on the evolving moving direction and density of trajectories. This decay function enables the evolving computation of accumulated outlying scores along the trajectories. An advantage of TOP-EYE is to identify evolving outliers at very early stage with relatively low false alarm rate. Finally, experimental results on real-world location traces show that TOP-EYE can effectively capture evolving abnormal trajectories.

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Top-Eye: top-k evolving trajectory outlier detection

An inventive arbiter controls access to a resource in a high speed computer or telecommunications network. The arbiter is capable of performing round-robin scheduling for N requests with P possible priority levels with a sublinear time complexity. The high arbitration speed is achieved through use of a tree structure with a token distribution system for implementing the round-robin scheduling policy.

Tree structured variable priority arbitration implementing a round-robin scheduling policy

A system for improving site operations by detecting abnormalities includes a first sensor abnormality detector connected to a first sensor and configured to learn a first normal behavior sequence, a second sensor abnormality detector connected to a second sensor and configured to learn a second normal behavior sequence, an abnormality correlation server configured to receive abnormally scored first sensor data and abnormally scored second sensor data, the abnormality correlation server further configured to correlate the received abnormally scored first sensor data and abnormally scored second sensor data sensed at the same time by the first and second sensors and determine an abnormal event; and an abnormality report generator configured to generate an abnormality report based on the correlated the received abnormally scored first sensor data and abnormally scored second sensor data.

Kuo Chu Lee

Papers

The Datacycle architecture

The datacycle architecture for very high throughput database systems

Top-Eye: top-k evolving trajectory outlier detection

Tree structured variable priority arbitration implementing a round-robin scheduling policy

System and method for improving site operations by detecting abnormalities