There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

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Data Mining: Concepts and Techniques (2nd edition)

We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.

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TinyDB: an acquisitional query processing system for sensor networks

Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large databases. In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner. This algorithm, called CVFDT, stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate. CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives, but with O(1) complexity per example, as opposed to O(w), where w is the size of the window. Experiments on a set of large time-changing data streams demonstrate the utility of this approach.

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Mining time-changing data streams

In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges. This article is an overview and survey of data stream algorithmics and is an updated version of [1].

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Data streams: algorithms and applications

With recent developments in Web technologies enabling interaction in virtual environments, as well as the ones in sensor network technologies enabling interaction with the real world, we see an emerging trend towards bringing these two worlds together. In this chapter, we share our experiences in building an RFID-supported library system on Second Life called SmartRFLib, which successfully achieves this integration. Although SmartRFLib focuses on a library system as an application scenario, it has been designed as a general-purpose RFID data management and complex event detection system, and can also be used as a basis to build other RFID-based event monitoring applications. DOI: 10.4018/978-1-60566-384-5.ch041

Connecting the Real World with the Virtual World: The SmartRFLib RFID-Supported Library System on Second Life

Query driven cardinality estimation models learn from a historical log of queries. They are lightweight, having low storage requirements, fast inference and training, and are easily adaptable for any kind of query. Unfortunately, such models can suffer unpredictably bad performance under workload drift, i.e., if the query pattern or data changes. This makes them unreliable and hard to deploy. We analyze the reasons why models become unpredictable due to workload drift, and introduce modifications to the query representation and neural network training techniques to make query-driven models robust to the effects of workload drift. First, we emulate workload drift in queries involving some unseen tables or columns by randomly masking out some table or column features during training. This forces the model to make predictions with missing query information, relying more on robust features based on up-to-date DBMS statistics that are useful even when query or data drift happens. Second, we introduce join bitmaps, which extends sampling-based features to be consistent across joins using ideas from sideways information passing. Finally, we show how both of these ideas can be adapted to handle data updates.
 
 We show significantly greater generalization than past works across different workloads and databases. For instance, a model trained with our techniques on a simple workload (JOBLight-train), with 40
 k
 synthetically generated queries of at most 3 tables each, is able to generalize to the much more complex Join Order Benchmark, which include queries with up to 16 tables, and improve query runtimes by 2× over PostgreSQL. We show similar robustness results with data updates, and across other workloads. We discuss the situations where we expect, and see, improvements, as well as more challenging workload drift scenarios where these techniques do not improve much over PostgreSQL. However, even in the most challenging scenarios, our models never perform worse than PostgreSQL, while standard query driven models can get much worse than PostgreSQL.


Robust Query Driven Cardinality Estimation under Changing Workloads

It is widely anticipated that time series data will dominate our future. From autonomous driving to industrial automation, the inevitable rise of the Internet of Things (IoT) has already exposed us to billions of data sources generating time-varying data at unprecedented complexity and scale. The opportunity to utilize this data reliably and efficiently to the benefit of the society critically depends on equipping our computing systems and their users with the right tools and capabilities. A key capability that lies at the core of managing time series data is the identification of patterns that signify “interesting" phenomena, such as anomalies. Anomalies are patterns in data that do not conform to expected (or, normal) behavior [1]. Detecting anomalies before, after, or as they occur not only finds use in many mission-critical domains, but also empowers systems and users with the ability to cope with large data volumes by guiding attention and resources to information that matters the most. Although anomaly detection has been actively studied for many decades, there are several challenges to be overcome before it can become practical in real-world deployments of time series applications, including the following: Anomaly detection is a highly domain-specific problem. What constitutes an anomaly changes greatly from one application domain to another. This bears the need to develop, compare, and choose from a variety of models and algorithms. Furthermore, domains may have different preferences for accuracy or performance tradeoffs (e.g., tolerance to different types of classification errors). Time series anomalies can be complex. They typically fall under the category of collective and contextual anomalies (as opposed to simpler, point anomalies) [1]. These anomalies may expose themselves at different time granularities or aggregations. Furthermore, anomalous patterns may vary over time due to temporal and dynamic nature of the data. Some time series datasets may even consist of multiple, potentially correlated attributes, requiring analyzing anomalies in a multi-variate fashion. Training data can be unavailable or noisy. In most domains, anomalies are relatively rare or unique events, making it challenging to collect anomalous datasets. Often, training datasets capture a small number of anomalous patterns, which may or may not be properly labeled. In IoT, presence of noisy sensor data makes this

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Tools for Advanced Time Series Analytics: Enabling the Future.

In this paper, we describe a scenario for a distributed marketplace whose scope can be the whole Web where resource discovery agents find out about resources that may want to join the marketplace and electronic commerce is realised through buying agents representing the customers and the selling agents representing the resources like electronic catalogs. The marketplace contains an Intelligent Directory Service (IDS) which makes it possible for agents to find out about each other and also contains references to the related Document Type Definitions (DTDs).

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An electronic marketplace architecture

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees.

Nesime Tatbul

Papers

Connecting the Real World with the Virtual World: The SmartRFLib RFID-Supported Library System on Second Life

Robust Query Driven Cardinality Estimation under Changing Workloads

Tools for Advanced Time Series Analytics: Enabling the Future.

An electronic marketplace architecture

S-Store: Streaming Meets Transaction Processing