939 research outputs found
๋์ฉ๋ ๋ฐ์ดํฐ ํ์์ ์ํ ์ ์ง์ ์๊ฐํ ์์คํ ์ค๊ณ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ,2020. 2. ์์ง์ฑ.Understanding data through interactive visualization, also known as visual analytics, is a common and necessary practice in modern data science. However, as data sizes have increased at unprecedented rates, the computation latency of visualization systems becomes a significant hurdle to visual analytics. The goal of this dissertation is to design a series of systems for progressive visual analytics (PVA)โa visual analytics paradigm that can provide intermediate results during computation and allow visual exploration of these resultsโto address the scalability hurdle. To support the interactive exploration of data with billions of records, we first introduce SwiftTuna, an interactive visualization system with scalable visualization and computation components. Our performance benchmark demonstrates that it can handle data with four billion records, giving responsive feedback every few seconds without precomputation. Second, we present PANENE, a progressive algorithm for the Approximate k-Nearest Neighbor (AKNN) problem. PANENE brings useful machine learning methods into visual analytics, which has been challenging due to their long initial latency resulting from AKNN computation. In particular, we accelerate t-Distributed Stochastic Neighbor Embedding (t-SNE), a popular non-linear dimensionality reduction technique, which enables the responsive visualization of data with a few hundred columns. Each of these two contributions aims to address the scalability issues stemming from a large number of rows or columns in data, respectively. Third, from the users' perspective, we focus on improving the trustworthiness of intermediate knowledge gained from uncertain results in PVA. We propose a novel PVA concept, Progressive Visual Analytics with Safeguards, and introduce PVA-Guards, safeguards people can leave on uncertain intermediate knowledge that needs to be verified. We also present a proof-of-concept system, ProReveal, designed and developed to integrate seven safeguards into progressive data exploration. Our user study demonstrates that people not only successfully created PVA-Guards on ProReveal but also voluntarily used PVA-Guards to manage the uncertainty of their knowledge. Finally, summarizing the three studies, we discuss design challenges for progressive systems as well as future research agendas for PVA.ํ๋ ๋ฐ์ดํฐ ์ฌ์ด์ธ์ค์์ ์ธํฐ๋ํฐ๋ธํ ์๊ฐํ๋ฅผ ํตํด ๋ฐ์ดํฐ๋ฅผ ์ดํดํ๋ ๊ฒ์ ํ์์ ์ธ ๋ถ์ ๋ฐฉ๋ฒ ์ค ํ๋์ด๋ค. ๊ทธ๋ฌ๋, ์ต๊ทผ ๋ฐ์ดํฐ์ ํฌ๊ธฐ๊ฐ ํญ๋ฐ์ ์ผ๋ก ์ฆ๊ฐํ๋ฉด์ ๋ฐ์ดํฐ ํฌ๊ธฐ๋ก ์ธํด ๋ฐ์ํ๋ ์ง์ฐ ์๊ฐ์ด ์ธํฐ๋ํฐ๋ธํ ์๊ฐ์ ๋ถ์์ ํฐ ๊ฑธ๋ฆผ๋์ด ๋์๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ์ด๋ฌํ ํ์ฅ์ฑ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ์ ์ง์ ์๊ฐ์ ๋ถ์(Progressive Visual Analytics)์ ์ง์ํ๋ ์ผ๋ จ์ ์์คํ
์ ๋์์ธํ๊ณ ๊ฐ๋ฐํ๋ค. ์ด๋ฌํ ์ ์ง์ ์๊ฐ์ ๋ถ์ ์์คํ
์ ๋ฐ์ดํฐ ์ฒ๋ฆฌ๊ฐ ์์ ํ ๋๋์ง ์๋๋ผ๋ ์ค๊ฐ ๋ถ์ ๊ฒฐ๊ณผ๋ฅผ ์ฌ์ฉ์์๊ฒ ์ ๊ณตํจ์ผ๋ก์จ ๋ฐ์ดํฐ์ ํฌ๊ธฐ๋ก ์ธํด ๋ฐ์ํ๋ ์ง์ฐ ์๊ฐ ๋ฌธ์ ๋ฅผ ์ํํ ์ ์๋ค. ์ฒซ์งธ๋ก, ์์ญ์ต ๊ฑด์ ํ์ ๊ฐ์ง๋ ๋ฐ์ดํฐ๋ฅผ ์๊ฐ์ ์ผ๋ก ํ์ํ ์ ์๋ SwiftTuna ์์คํ
์ ์ ์ํ๋ค. ๋ฐ์ดํฐ ์ฒ๋ฆฌ ๋ฐ ์๊ฐ์ ํํ์ ํ์ฅ์ฑ์ ๋ชฉํ๋ก ๊ฐ๋ฐ๋ ์ด ์์คํ
์, ์ฝ 40์ต ๊ฑด์ ํ์ ๊ฐ์ง ๋ฐ์ดํฐ์ ๋ํ ์๊ฐํ๋ฅผ ์ ์ฒ๋ฆฌ ์์ด ์ ์ด๋ง๋ค ์
๋ฐ์ดํธํ ์ ์๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ๋์งธ๋ก, ๊ทผ์ฌ์ k-์ต๊ทผ์ ์ (Approximate k-Nearest Neighbor) ๋ฌธ์ ๋ฅผ ์ ์ง์ ์ผ๋ก ๊ณ์ฐํ๋ PANENE ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ๊ทผ์ฌ์ k-์ต๊ทผ์ ์ ๋ฌธ์ ๋ ์ฌ๋ฌ ๊ธฐ๊ณ ํ์ต ๊ธฐ๋ฒ์์ ์ฐ์์๋ ๋ถ๊ตฌํ๊ณ ์ด๊ธฐ ๊ณ์ฐ ์๊ฐ์ด ๊ธธ์ด์ ์ธํฐ๋ํฐ๋ธํ ์์คํ
์ ์ ์ฉํ๊ธฐ ํ๋ ํ๊ณ๊ฐ ์์๋ค. PANENE ์๊ณ ๋ฆฌ์ฆ์ ์ด๋ฌํ ๊ธด ์ด๊ธฐ ๊ณ์ฐ ์๊ฐ์ ํ๊ธฐ์ ์ผ๋ก ๊ฐ์ ํ์ฌ ๋ค์ํ ๊ธฐ๊ณ ํ์ต ๊ธฐ๋ฒ์ ์๊ฐ์ ๋ถ์์ ํ์ฉํ ์ ์๋๋ก ํ๋ค. ํนํ, ์ ์ฉํ ๋น์ ํ์ ์ฐจ์ ๊ฐ์ ๊ธฐ๋ฒ์ธ t-๋ถํฌ ํ๋ฅ ์ ์๋ฒ ๋ฉ(t-Distributed Stochastic Neighbor Embedding)์ ๊ฐ์ํ์ฌ ์๋ฐฑ ๊ฐ์ ์ฐจ์์ ๊ฐ์ง๋ ๋ฐ์ดํฐ๋ฅผ ๋น ๋ฅธ ์๊ฐ ๋ด์ ์ฌ์ํ ์ ์๋ค. ์์ ๋ ์์คํ
๊ณผ ์๊ณ ๋ฆฌ์ฆ์ด ๋ฐ์ดํฐ์ ํ ๋๋ ์ด์ ๊ฐ์๋ก ์ธํ ํ์ฅ์ฑ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ณ ์ ํ๋ค๋ฉด, ์ธ ๋ฒ์งธ ์์คํ
์์๋ ์ ์ง์ ์๊ฐ์ ๋ถ์์ ์ ๋ขฐ๋ ๋ฌธ์ ๋ฅผ ๊ฐ์ ํ๊ณ ์ ํ๋ค. ์ ์ง์ ์๊ฐ์ ๋ถ์์์ ์ฌ์ฉ์์๊ฒ ์ฃผ์ด์ง๋ ์ค๊ฐ ๊ณ์ฐ ๊ฒฐ๊ณผ๋ ์ต์ข
๊ฒฐ๊ณผ์ ๊ทผ์ฌ์น์ด๋ฏ๋ก ๋ถํ์ค์ฑ์ด ์กด์ฌํ๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ์ธ์ดํ๊ฐ๋๋ฅผ ์ด์ฉํ ์ ์ง์ ์๊ฐ์ ๋ถ์(Progressive Visual Analytics with Safeguards)์ด๋ผ๋ ์๋ก์ด ๊ฐ๋
์ ์ ์ํ๋ค. ์ด ๊ฐ๋
์ ์ฌ์ฉ์๊ฐ ์ ์ง์ ํ์์์ ๋ง์ฃผํ๋ ๋ถํ์คํ ์ค๊ฐ ์ง์์ ์ธ์ดํ๊ฐ๋๋ฅผ ๋จ๊ธธ ์ ์๋๋ก ํ์ฌ ํ์์์ ์ป์ ์ง์์ ์ ํ๋๋ฅผ ์ถํ ๊ฒ์ฆํ ์ ์๋๋ก ํ๋ค. ๋ํ, ์ด๋ฌํ ๊ฐ๋
์ ์ค์ ๋ก ๊ตฌํํ์ฌ ํ์ฌํ ProReveal ์์คํ
์ ์๊ฐํ๋ค. ProReveal๋ฅผ ์ด์ฉํ ์ฌ์ฉ์ ์คํ์์ ์ฌ์ฉ์๋ค์ ์ธ์ดํ๊ฐ๋๋ฅผ ์ฑ๊ณต์ ์ผ๋ก ๋ง๋ค ์ ์์์ ๋ฟ๋ง ์๋๋ผ, ์ค๊ฐ ์ง์์ ๋ถํ์ค์ฑ์ ๋ค๋ฃจ๊ธฐ ์ํด ์ธ์ดํ๊ฐ๋๋ฅผ ์๋ฐ์ ์ผ๋ก ์ด์ฉํ๋ค๋ ๊ฒ์ ์ ์ ์์๋ค. ๋ง์ง๋ง์ผ๋ก, ์ ์ธ ๊ฐ์ง ์ฐ๊ตฌ์ ๊ฒฐ๊ณผ๋ฅผ ์ข
ํฉํ์ฌ ์ ์ง์ ์๊ฐ์ ๋ถ์ ์์คํ
์ ๊ตฌํํ ๋์ ๋์์ธ์ ๋์ ์ ํฅํ ์ฐ๊ตฌ ๋ฐฉํฅ์ ๋ชจ์ํ๋ค.CHAPTER1. Introduction 2
1.1 Background and Motivation 2
1.2 Thesis Statement and Research Questions 5
1.3 Thesis Contributions 5
1.3.1 Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 6
1.3.2 ProgressiveComputation of Approximate k-Nearest Neighbors and Responsive t-SNE 7
1.3.3 Progressive Visual Analytics with Safeguards 8
1.4 Structure of Dissertation 9
CHAPTER2. Related Work 11
2.1 Progressive Visual Analytics 11
2.1.1 Definitions 11
2.1.2 System Latency and Human Factors 13
2.1.3 Users, Tasks, and Models 15
2.1.4 Techniques, Algorithms, and Systems. 17
2.1.5 Uncertainty Visualization 19
2.2 Approaches for Scalable Visualization Systems 20
2.3 The k-Nearest Neighbor (KNN) Problem 22
2.4 t-Distributed Stochastic Neighbor Embedding 26
CHAPTER3. SwiTuna: Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 28
3.1 The SwiTuna Design 31
3.1.1 Design Considerations 32
3.1.2 System Overview 33
3.1.3 Scalable Visualization Components 36
3.1.4 Visualization Cards 40
3.1.5 User Interface and Interaction 42
3.2 Responsive Querying 44
3.2.1 Querying Pipeline 44
3.2.2 Prompt Responses 47
3.2.3 Incremental Processing 47
3.3 Evaluation: Performance Benchmark 49
3.3.1 Study Design 49
3.3.2 Results and Discussion 52
3.4 Implementation 56
3.5 Summary 56
CHAPTER4. PANENE:AProgressive Algorithm for IndexingandQuerying Approximate k-Nearest Neighbors 58
4.1 Approximate k-Nearest Neighbor 61
4.1.1 A Sequential Algorithm 62
4.1.2 An Online Algorithm 63
4.1.3 A Progressive Algorithm 66
4.1.4 Filtered AKNN Search 71
4.2 k-Nearest Neighbor Lookup Table 72
4.3 Benchmark. 78
4.3.1 Online and Progressive k-d Trees 78
4.3.2 k-Nearest Neighbor Lookup Tables 83
4.4 Applications 85
4.4.1 Progressive Regression and Density Estimation 85
4.4.2 Responsive t-SNE 87
4.5 Implementation 92
4.6 Discussion 92
4.7 Summary 93
CHAPTER5. ProReveal: Progressive Visual Analytics with Safeguards 95
5.1 Progressive Visual Analytics with Safeguards 98
5.1.1 Definition 98
5.1.2 Examples 101
5.1.3 Design Considerations 103
5.2 ProReveal 105
5.3 Evaluation 121
5.4 Discussion 127
5.5 Summary 130
CHAPTER6. Discussion 132
6.1 Lessons Learned 132
6.2 Limitations 135
CHAPTER7. Conclusion 137
7.1 Thesis Contributions Revisited 137
7.2 Future Research Agenda 139
7.3 Final Remarks 141
Abstract (Korean) 155
Acknowledgments (Korean) 157Docto
Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques
Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application
Efficient bulk-loading methods for temporal and multidimensional index structures
Nahezu alle naturwissenschaftlichen Bereiche profitieren von neuesten Analyse- und Verarbeitungsmethoden fรผr groรe Datenmengen. Diese Verfahren setzten eine effiziente Verarbeitung von geo- und zeitbezogenen Daten voraus, da die Zeit und die Position wichtige Attribute vieler Daten
sind. Die effiziente Anfrageverarbeitung wird insbesondere durch den Einsatz von Indexstrukturen
ermรถglicht. Im Fokus dieser Arbeit liegen zwei Indexstrukturen: Multiversion B-Baum
(MVBT) und R-Baum. Die erste Struktur wird fรผr die Verwaltung von zeitbehafteten Daten,
die zweite fรผr die Indexierung von mehrdimensionalen Rechteckdaten eingesetzt.
Stรคndig- und schnellwachsendes Datenvolumen stellt eine groรe Herausforderung an die Informatik
dar. Der Aufbau und das Aktualisieren von Indexen mit herkรถmmlichen Methoden (Datensatz
fรผr Datensatz) ist nicht mehr effizient. Um zeitnahe und kosteneffiziente Datenverarbeitung
zu ermรถglichen, werden Verfahren zum schnellen Laden von Indexstrukturen dringend benรถtigt.
Im ersten Teil der Arbeit widmen wir uns der Frage, ob es ein Verfahren fรผr das Laden von MVBT
existiert, das die gleiche I/O-Komplexitรคt wie das externe Sortieren besitz. Bis jetzt blieb diese
Frage unbeantwortet. In dieser Arbeit haben wir eine neue Kostruktionsmethode entwickelt und
haben gezeigt, dass diese gleiche Zeitkomplexitรคt wie das externe Sortieren besitzt. Dabei haben
wir zwei algorithmische Techniken eingesetzt: Gewichts-Balancierung und Puffer-Bรคume. Unsere
Experimenten zeigen, dass das Resultat nicht nur theoretischer Bedeutung ist.
Im zweiten Teil der Arbeit beschรคftigen wir uns mit der Frage, ob und wie statistische Informationen
รผber Geo-Anfragen ausgenutzt werden kรถnnen, um die Anfrageperformanz von R-Bรคumen zu
verbessern. Unsere neue Methode verwendet Informationen wie Seitenverhรคltnis und Seitenlรคngen
eines reprรคsentativen Anfragerechtecks, um einen guten R-Baum bezรผglich eines hรคufig eingesetzten
Kostenmodells aufzubauen. Falls diese Informationen nicht verfรผgbar sind, optimieren
wir R-Bรคume bezรผglich der Summe der Volumina von minimal umgebenden Rechtecken der Blattknoten.
Da das Problem des Aufbaus von optimalen R-Bรคumen bezรผglich dieses Kostenmaรes
NP-hart ist, fรผhren wir zunรคchst das Problem auf ein eindimensionales Partitionierungsproblem
zurรผck, indem wir die Daten bezรผglich optimierte raumfรผllende Kurven sortieren. Dann lรถsen
wir dieses Problem durch Einsatz vom dynamischen Programmieren. Die I/O-Komplexitรคt des
Verfahrens ist gleich der von externem Sortieren, da die I/O-Laufzeit der Methode durch die
Laufzeit des Sortierens dominiert wird.
Im letzten Teil der Arbeit haben wir die entwickelten Partitionierungsvefahren fรผr den Aufbau
von Geo-Histogrammen eingesetzt, da diese รคhnlich zu R-Bรคumen eine disjunkte Partitionierung
des Raums erzeugen. Ergebnisse von intensiven Experimenten zeigen, dass sich unter Verwendung
von neuen Partitionierungstechniken sowohl R-Bรคume mit besserer Anfrageperformanz als
auch Geo-Histogrammen mit besserer Schรคtzqualitรคt im Vergleich zu Konkurrenzverfahren generieren
lassen
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Compiling Communication-Minimizing Query Plans
Because of the low arithmetic intensity of relational database operators, the performance of in-memory column stores ought to be bound by main-memory bandwidth, and in practice, highly-optimized operator implementations already achieve close to their peak theoretical performance. By itself, this would imply that hardware acceleration for analytics would be of limited utility, but I show that the emergence of full-query compilation presents new opportunities to reduce memory traffic and trade computation for communication, meaning that database-oriented processors may yet be worth designing.Moreover, the communication costs of queries on a given processor and memory hierarchy are determined by factors below the level of abstraction expressed in traditional query plans, such as how operators are (or are not) fused together, how execution is parallelized and cache-blocked, and how intermediate results are arranged in memory. I present a Scala- embedded programming language called Ressort that exposes these machine-level aspects of query compilation, and which emits parallel C++/OpenMP code as its target to express a greater range of algorithmic variants for each query than would be easy to study by hand
Backpropagated Gradient Representations for Anomaly Detection
Learning representations that clearly distinguish between normal and abnormal
data is key to the success of anomaly detection. Most of existing anomaly
detection algorithms use activation representations from forward propagation
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Gradients capture model updates required to represent data. Anomalies require
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benchmark image recognition datasets. Also, we highlight the computational
efficiency and the simplicity of the proposed method in comparison with other
state-of-the-art methods relying on adversarial networks or autoregressive
models, which require at least 27 times more model parameters than the proposed
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A Pre-trained Data Deduplication Model based on Active Learning
In the era of big data, the issue of data quality has become increasingly
prominent. One of the main challenges is the problem of duplicate data, which
can arise from repeated entry or the merging of multiple data sources. These
"dirty data" problems can significantly limit the effective application of big
data. To address the issue of data deduplication, we propose a pre-trained
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semantic level. The model is built on a pre-trained Transformer and fine-tuned
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firstly integrate the transformer with active learning into an end-to-end
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Discrete Optimization Methods for Segmentation and Matching
This dissertation studies discrete optimization methods for several computer vision problems. In the first part, a new objective function for superpixel segmentation is proposed. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes. I present a new graph construction for images and show that this construction induces a matroid. The segmentation is then given by the graph topology which maximizes the objective function under the matroid constraint. By exploiting submodular and monotonic properties of the objective function, I develop an efficient algorithm with a worst-case performance bound of for the superpixel segmentation problem. Extensive experiments on the Berkeley segmentation benchmark show the proposed algorithm outperforms the state of the art in all the standard evaluation metrics.
Next, I propose a video segmentation algorithm by maximizing a submodular objective function subject to a matroid constraint. This function is similar to the standard energy function in computer vision with unary terms, pairwise terms from the Potts model, and a novel higher-order term based on appearance histograms. I show that the standard Potts model prior, which becomes non-submodular for multi-label problems, still induces a submodular function in a maximization framework. A new higher-order prior further enforces consistency in the appearance histograms both spatially and temporally across the video. The matroid constraint leads to a simple algorithm with a performance bound of . A branch and bound procedure is also presented to improve the solution computed by the algorithm.
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Performance evaluation of word-aligned compression methods for bitmap indices
Bitmap indices are a widely used scheme for large read-only repositories in data warehouses and scientific databases. This binary representation allows the use of bit-wise operations for fast query processing and is typically compressed using run-length encoding techniques. Most bitmap compression techniques are aligned using a fixed encoding length (32 or 64 bits) to avoid explicit decompression during query time. They have been proposed to extend or enhance word-aligned hybrid (WAH) compression. This paper presents a comparative study of four bitmap compression techniques: WAH, PLWAH, CONCISE, and EWAH. Experiments are targeted to identify the conditions under which each method should be applied and quantify the overhead incurred during query processing. Performance in terms of compression ratio and query time is evaluated over synthetic-generated bitmap indices, and results are validated over bitmap indices generated from real data sets. Different query optimizations are explored, query time estimation formulas are defined, and the conditions under which one method should be preferred over another are formalized
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