Scalable Keyword Search on Large Data Streams

In this paper we study the problem of constructing histograms from high-speed time-changing data streams. Learning in this context requires the ability to process examples once at the rate they arrive, maintaining a histogram consistent with the most recent data, and forgetting out-date data whenever a change in the distribution is detected. To construct histogram from high-speed data streams we use the two layer structure used in the Partition Incremental Discretization (PiD) algorithm. Our contribution is a new method to detect whenever a change in the distribution generating examples occurs. The base idea consists of monitoring distributions from two different time windows: the reference time window, that reflects the distribution observed in the past; and the current time window reflecting the distribution observed in the most recent data. We compare both distributions and signal a change whenever they are greater than a threshold value, using three different methods: the Entropy Absolute Difference, the Kullback-Leibler divergence and the Cosine Distance. The experimental results suggest that Kullback-Leibler divergence exhibit high probability in change detection, faster detection rates, with few false positives alarms.

Multimedia synchronization comprises both the definition and the establishment of temporal relationships among media types. The presentation of 'in sync' data streams is essential to achieve a natural impression, data that is 'out of sync' is perceived as being somewhat artificial, strange, or even annoying. Therefore, the goal of any multimedia system is to enable an application to present data without no or little synchronization errors. The achievement of this goal requires a detailed knowledge of the synchronization requirements at the user interface. The paper presents the results of a series of experiments about human media perception that may be used as 'quality of service' guidelines. The results show that skews between related data streams may still give the effect that the data is 'in sync' and gives some constraints under which jitter may be tolerated. The author uses the findings to develop a scheme for the processing of nontrivial synchronization skew between more than two data streams.

Human Perception of Media Synchronization

A 28 nm CMOS Software-Defined Transceiver (SDTRX) enabling High-Speed Data (HSD) streaming, including Ultra HD TV, within home cable networks is presented. By making efficient use of available cable bandwidth, the SDTRX dynamically handles up to 1024QAM OFDM-modulated HSD streams. This paper addresses SDTRX system-level design methodology as the key driver in enabling performance optimization for achieving a wide frequency range of operation at the lowest power and area consumption. By employing an optimized architecture constructed on available state-of-the art 28 nm functional building blocks, the monolithic SDTRX consists of a mixer-based harmonic rejection RX with a DAC-based TX and a smart PLL system. It operates over a 0.4-to-1.7 GHz frequency range while consuming less than 475 mW in half-duplex mode. Moreover, by developing a simple TX-RX loopback circuit, the system is enabled to efficiently calibrate TX output power and to remove the need for a dedicated external pin. This low-cost SDTRX is embedded in various 28 nm CMOS multimedia SoCs and is, to the authors' knowledge, the first reported transceiver front end to enable true high-speed data streaming within home cable networks.

Histograms are a common technique for density estimation and they have been widely used as a tool in exploratory data analysis. Learning histograms from static and stationary data is a well known topic. Nevertheless, very few works discuss this problem when we have a continuous flow of data generated from dynamic environments. The scope of this paper is to detect changes from high-speed time-changing data streams. To address this problem, we construct histograms able to process examples once at the rate they arrive. The main goal of this work is continuously maintain a histogram consistent with the current status of the nature. We study strategies to detect changes in the distribution generating examples, and adapt the histogram to the most recent data by forgetting outdated data. We use the Partition Incremental Discretization algorithm that was designed to learn histograms from high-speed data streams. We present a method to detect whenever a change in the distribution generating examples occurs. The base idea consists of monitoring distributions from two different time windows: the reference window, reflecting the distribution observed in the past; and the current window which receives the most recent data. The current window is cumulative and can have a fixed or an adaptive step depending on the distance between distributions. We compared both distributions using Kullback-Leibler divergence, defining a threshold for change detection decision based on the asymmetry of this measure. We evaluated our algorithm with controlled artificial data sets and compare the proposed approach with nonparametric tests. We also present results with real word data sets from industrial and medical domains. Those results suggest that an adaptive window’s step exhibit high probability in change detection and faster detection rates, with few false positives alarms.

Discovering interesting patterns from high-speed data streams is a challenging problem in data mining. Recently, the support metric-based frequent pattern mining from data stream has achieved a great attention. However, the occurrence frequency of a pattern may not be an appropriate criterion for discovering meaningful patterns. Temporal regularity in occurrence behavior can be a key criterion for assessing the importance of patterns in several online applications such as market basket analysis, gene data analysis, network monitoring, and stock market. A pattern can be said regular if its occurrence behavior satisfies a user-given interval in the data steam. Mining regular patterns from static databases has recently been addressed. However, even though mining regular patterns from stream data is extremely required in online applications, no such algorithm has been proposed yet. Therefore, in this paper we develop a novel tree structure called Regular Pattern Stream tree (RPS-tree), and an efficient mining technique for discovering regular patterns over data stream. Using a sliding window method the RPS-tree captures the stream content, and with an efficient tree updating mechanism it constantly processes exact stream data when the stream flows. Extensive experimental analyses show that our RPS-tree is highly efficient in discovering regular patterns from a high-speed data stream.

Distributed systems have been widely used for applications that need real-time processing over high volume and high speed data streams. This work presents the architecture and the implementation of a correlation algorithm for streaming data on a Graphic Processing Unit (GPU). The proposed system accelerates the correlation calculation of the Hayashi-Yoshida algorithm up to 10x vs. a conventional distributed system, and these performance characteristics apply to a broad category of correlation estimators. Furthermore, our system offers real-time correlation computation over high-speed streaming data and demonstrates how a batch processing algorithm can be applied to real-time streaming data. The results show that GPUs are a highly promising platform for correlation estimators as they improve significantly the volume of streaming data that can be processed in real time vs. other approaches that use “unlimited” conventional computing resources.

Outlier detection or anomaly detection is applied in numerous applications, such as fraud detection, network intrusion detection, manufacturing, and environmental monitoring. Due to the continuous and dynamic characteristics of streaming data, outlier detection over data streams becomes a very challenging task. When analyzing real-time data streams, it is typically impossible to store the entire set of data due to space limitations. Also due to the high data rates, it is necessary to produce the results in a limited amount of time. Parallel processing power of Graphics Processing Units (GPUs) can be used to accelerate the outlier detection process, and thus address the challenges of outlier detection over data streams. This paper proposes a GPU-accelerated outlier detection algorithm for continuous data streams using kernel density estimation approach. Experiments show that the proposed SOD_GPU algorithm is efficient for detecting outliers in high-dimensional, high-speed data streams, and produces results in a timely manner without compromising the outlier detection accuracy. The proposed method achieved up to 20X speedup compared to a respective multi-core CPU implementation, and the speedup increases with the number of data attributes and the input data rate.

The Web has become a large repository of documents (or pages) written in many different languages. In this context, traditional information retrieval (IR) techniques cannot be used whenever the user query and the documents being retrieved are in different languages. To address this problem, new cross‐language information retrieval (CLIR) techniques have been proposed. In this work, we describe a method for cross‐language retrieval of medical information. This method combines query terms and related medical concepts obtained automatically through a categorization procedure. The medical concepts are used to create a linguistic abstraction that allows retrieval of information in a language‐independent way, minimizing linguistic problems such as polysemy. To evaluate our method, we carried out experiments using the OHSUMED test collection, whose documents are written in English, with queries expressed in Portuguese, Spanish, and French. The results indicate that our cross‐language retrieval method is as effective as a standard vector space model algorithm operating on queries and documents in the same language. Further, our results are better than previous results in the literature.

The methods of approximate query processing have been proposed for analytics over high-speed data streams, which compact continuous streams into a space-constrained sketch and provide reliable estimates for different queries. Count-Min (CM) is the state-of-the-art sketching structure supporting many queries with error-guaranteed estimates under limited space. However, we need to create a counter table beforehand in CM according to the size of data streams, while it is usually unpredictable for dynamic data streams. In this paper, we proposed an approach, called Dynamic Count-Min sketch (DCM), which is appropriate for dynamic data set and can provide accurate estimates for point query and self-join size query. Our approach constitutes incremental CM sketches and allocates space in a pay-as-you-go manner. Our mathematical analysis and substantial experiments both show that our approach is appropriate for data sets with dynamic or skewed inputs and can provide error-guaranteed estimates with less space compared to CM.

We witness a rapid increase in the number of social media streams due to development of Web2.0, IoT and Cloud Computing technology. These sources include both traditional relational databases and streaming data from messaging infrastructure. We would like to use multiple social media streams to answer complex queries to enable information sharing and intelligence gathering for better collaboration. For this purpose, we adopt data services as the basic abstraction for both traditional relational databases and data streams retrieval. A flexible continuous data service model with continuous query as service operation is proposed. Service operation instance is modeled as a view defined on data streams. In the view, data part and time synchronization part are separated from each other. Based on the continuous data service model, we proposed a continuous data service composition algorithm for answering queries across data streams and relational data. The main idea is to find the contained rewriting of user query on views satisfying both data part and time synchronization part containment relationship. We also present use case and experimental studies that indicate that the approach is effective and efficient.

Nowadays in many real-world scenarios, high speed data streams are usually with non-uniform misclassification costs and thus call for cost-sensitive classification algorithms of data streams. However, only little literature focuses on this issue. On the other hand, the existing algorithms for cost-sensitive classification can achieve excellent performance in the metric of total misclassification costs, but always lead to obvious reduction of accuracy, which restrains the practical application greatly. In this paper, we present an improved folk theorem. Based on the new theorem, the existing accuracy-based classification algorithm can be converted into soft cost-sensitive one immediately, which allows us to take both accuracy and cost into account. Following the idea of this theorem, the soft-CsGDT algorithm is proposed to process the data streams with non-uniform misclassification costs, which is an expansion of GDT. With both synthetic and real-world datasets, the experimental results show that compared with the cost-sensitive algorithm, the accuracy in our soft-CsGDT is significantly improved, while the total misclassification costs are approximately the same.

Observations of information retrieval (IR) system user experiences reveal a strong desire for collaborative search while at the same time suggesting that collaborative capabilities are rarely, and then only in a limited fashion, supported by current searching and visualization tools. Equally interesting is the fact that observations of user experiences with group support systems (GSS) reveal that although access to external information and the ability to search for relevant material is often vital to the progress of GSS sessions, integrated support for collaborative searching and visualization of results is lacking in GSS systems. After reviewing both user experiences described in IR and GSS literature and observing and interviewing users of existing IR and GSS commercial and prototype systems, the authors conclude that there is an obvious demand for systems supporting multi-user IR. It is surprising to the authors that very little attention has been given to the common ground shared by these two important research domains. With this in mind, our paper describes how user experiences with IR and GSS systems have shed light on a promising new area of collaborative research and led to the development of a prototype that merges the two paradigms into a collaborative information retrieval environment (CIRE). The paper presents a theory developed from initial user experiences with our prototype and describes plans to test the efficacy of this new paradigm empirically through controlled experimentation.

In data streams, persistence characterizes items that appear repeatedly across multiple non-overlapping time windows. Addressing persistence-based tasks, such as detecting highly persistent items and estimating persistence, is crucial for applications like recommendation systems and anomaly detection in high-velocity data streams. However, these tasks are challenging due to stringent requirements for rapid processing and limited memory resources. Existing methods often struggle with accuracy, especially given highly skewed data distributions and tight fastest memory budgets, where hash collisions are severe. In this paper, we introduce Pandora, a novel approximate data structure designed to tackle these challenges efficiently. Our approach incorporates the insight that items absent for extended periods are likely non-persistent, increasing their probability of eviction to accommodate potential persistent items more effectively. We validate this insight empirically and integrate it into our update strategy, providing better protection for persistent items. We formally analyze Pandora's error bounds to validate its theoretical soundness. Through extensive trace-driven tests, we demonstrate that Pandora achieves superior accuracy and processing speed compared to state-of-the-art methods across various persistence-based tasks. Additionally, we further accelerate Pandora's update speed using Single Instruction Multiple Data (SIMD) instructions, enhancing its efficiency in high-speed data stream environments. The code for our method is open-sourced.

The Web enables to have access to silo-ed information describing news articles, often offering a multitude of viewpoints that, once combined, can provide a broader picture of the story being reported on the news. In this paper, we propose an approach that automatically extracts representative features of a news item, namely named entities, from textual content attached to a video item (subtitles) and from a set of documents from the Web collected using entity expansion techniques. Approaches relying on entity expansion generally try to collect and process the important facts behinds a particular news item, but they are often too dependent on frequency-based functions and information retrieval techniques thus neglecting the multi-dimensional relationships that are established among the entities. We propose a concentric-based approach that enables to represent the context of a news item, by harmonizing into a single model the representative entities, which can be extracted using information retrieval and natural language processing techniques (Core), and other entities that get prominent according to different dimensions such as informativeness, semantic connectivity, or popularity (Crust). We compare our approach with a baseline by analyzing the compactness of the generated summary on an existing gold standard available on the Web. Results of the experiments show that our approach converges faster to the ideal compact news snapshot with an improvement of 36.9% over the baseline.