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.

/pdf/tinydb-an-acquisitional-query-processing-system-for-sensor-2akih312yn.pdf

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.

/pdf/mining-time-changing-data-streams-a9kghvc13p.pdf

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

Background: Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics. Genomics data analytics at this scale requires overcoming performance bottlenecks, such as searching for short DNA sequences over long reference sequences. Results: In this paper, we introduce LISA (Learned Indexes for Sequence Analysis), a novel learning-based approach to DNA sequence search. We focus on accelerating two of the most essential flavors of DNA sequence search-- exact search and super-maximal exact match (SMEM) search. LISA builds on and extends FM-index, which is the state-of-the-art technique widely deployed in genomics tools. Experiments with human, animal, and plant genome datasets indicate that LISA achieves up to 2.2x and 13.3x speedups over the state-of-the-art FM-index based implementations for exact search and super-maximal exact match (SMEM) search, respectively.

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LISA: Learned Indexes for DNA Sequence Analysis

Integrating low-latency data streaming into data warehouse architectures has become an important enhancement to support modern data warehousing applications. In these architectures, heterogeneous workloads with data ingestion and analytical queries must be executed with strict performance guarantees. Furthermore, the data warehouse may consists of multiple different types of storage engines (a.k.a., polystores or multi-stores). A paramount problem is data placement; different workload scenarios call for different data placement designs. Moreover, workload conditions change frequently. In this paper, we provide evidence that a dynamic, workload-driven approach is needed for data placement in polystores with low-latency data ingestion support. We study the problem based on the characteristics of the TPC-DI benchmark in the context of an abbreviated polystore that consists of S-Store and Postgres.

https://dl.acm.org/doi/pdf/10.1145/3129292.3129297

Towards Dynamic Data Placement for Polystore Ingestion

Code similarity systems are integral to a range of applications from code recommendation to automated software defect correction. We argue that code similarity is now a first-order problem that must be solved. To begin to address this, we present machine Inferred Code Similarity (MISIM), a novel end-to-end code similarity system that consists of two core components. First, MISIM uses a novel context-aware semantic structure, which is designed to aid in lifting semantic meaning from code syntax. Second, MISIM provides a neural-based code similarity scoring algorithm, which can be implemented with various neural network architectures with learned parameters. We compare MISIM to three state-of-the-art code similarity systems: (i)code2vec, (ii)Neural Code Comprehension, and (iii)Aroma. In our experimental evaluation across 328,155 programs (over 18 million lines of code), MISIM has 1.5x to 43.4x better accuracy than all three systems.

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MISIM: A Novel Code Similarity System

In this work, we model the problem of online event detection in microblogs as a stateful stream processing problem and offer a novel solution that balances result accuracy and performance. Our new approach builds on two state of the art algorithms. The first algorithm is based on identifying bursty keywords inside blocks of blog messages. The second one involves clustering blog messages based on similarity of their contents. To combine the computational simplicity of the keyword-based algorithm with the semantic accuracy of the clustering-based algorithm, we propose a new hybrid algorithm. We then implement these algorithms in a streaming manner, on top of Apache Storm augmented with Apache Cassandra for state management. Experiments with a 12M tweet dataset from Twitter show that our hybrid approach provides a better accuracy-performance compromise than the previous approaches.

Streaming Event Detection in Microblogs: Balancing Accuracy and Performance

Access to high-quality data repositories and benchmarks have been instrumental in advancing the state of the art in many domains, as they provide the research community a common ground for training, testing, evaluating, comparing, and experimenting with novel machine learning models. Lack of such community resources for anomaly detection (AD) severely limits progress. In this report, we present AnomalyBench, the first comprehensive benchmark for explainable AD over high-dimensional (2000+) time series data. AnomalyBench has been systematically constructed based on real data traces from ~100 repeated executions of 10 large-scale stream processing jobs on a Spark cluster. 30+ of these executions were disturbed by introducing ~100 instances of different types of anomalous events (e.g., misbehaving inputs, resource contention, process failures). For each of these anomaly instances, ground truth labels for the root-cause interval as well as those for the effect interval are available, providing a means for supporting both AD tasks and explanation discovery (ED) tasks via root-cause analysis. We demonstrate the key design features and practical utility of AnomalyBench through an experimental study with three state-of-the-art semi-supervised AD techniques.

Nesime Tatbul

Papers

LISA: Learned Indexes for DNA Sequence Analysis

Towards Dynamic Data Placement for Polystore Ingestion

MISIM: A Novel Code Similarity System

Streaming Event Detection in Microblogs: Balancing Accuracy and Performance

AnomalyBench: An Open Benchmark for Explainable Anomaly Detection.