arXiv subjects

Chunbin Lin

Publications and source records attributed to Chunbin Lin.

7 recordsLinked to original sources

arXiv2019

Improving Distributed Similarity Join in Metric Space with Error-bounded Sampling

Given two sets of objects, metric similarity join finds all similar pairs of objects according to a particular distance function in metric space. There is an increasing demand to provide a scalable similarity join framework which can support efficient query and analytical services in the era of Big Data. The existing distributed metric similarity join algorithms adopt random sampling techniques to produce pivots and utilize holistic partitioning methods based on the generated pivots to partition data, which results in data skew problem since both the generated pivots and the partition strategies have no quality guarantees. To address the limitation, we propose SP-Join, an end-to-end framework to support distributed similarity join in metric space based on the MapReduce paradigm, which (i) employs an estimation-based stratified sampling method to produce pivots with quality guarantees for any sample size, and (ii) devises an effective cost model as the guideline to split the whole datasets into partition in map and reduce phases according to the sampled pivots. Although obtaining an optimal set of partitions is proven to be NP-Hard, SP-Join adopts efficient partitioning strategies based on such a cost model to achieve an even partition with explainable quality. We implement our framework upon Apache Spark platform and conduct extensive experiments on four real-world datasets. The results show that our method significantly outperforms state-of-the-art methods.

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arXiv2018

Plato: Approximate Analytics over Compressed Time Series with Tight Deterministic Error Guarantees

Plato provides fast approximate analytics on time series, by precomputing and storing compressed time series. Plato's key novelty is the delivery of tight deterministic error guarantees for time series analytics. Plato evaluates any time series expression composed by the linear algebra operators over vectors, along with arithmetic operators. This large scope of possible expressions includes common use cases such as correlation and cross-correlation expressions. Each time series is segmented either by fixed-length segmentation or by (a usually more effective) variable-length segmentation. Each segment is compressed by an estimation/compression function that approximates the actual values and is coming from a user-chosen function family, as taught by many prior works. The novelty is that Plato associates to each segment 1 to 3 (depending on the case) precomputed error measures and, using them, Plato computes tight deterministic error guarantees for analytics over the compressions. Importantly, some compression families lead to much better deterministic error guarantees. This work identifies two broad estimation function family groups (Vector Space (VS) and Linear Scalable Family (LSF)), which lead to theoretically and practically high-quality guarantees, even for expressions (eg correlation) that combine multiple time series that have been independently compressed and may, thus, use misaligned segmentations. The theoretical aspect of "high quality" is crisply captured by the Amplitude Independence (AI) property: An AI guarantee does not depend on the amplitude of the involved time series, even when we combine multiple time series. The experiments on four real-life datasets showed that when the novel AI guarantees were applicable, the approximate query results were certified to be very close (typically 1%) to the true results.

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arXiv2017

Efficient Approximate Query Answering over Sensor Data with Deterministic Error Guarantees

With the recent proliferation of sensor data, there is an increasing need for the efficient evaluation of analytical queries over multiple sensor datasets. The magnitude of such datasets makes exact query answering infeasible, leading researchers into the development of approximate query answering approaches. However, existing approximate query answering algorithms are not suited for the efficient processing of queries over sensor data, as they exhibit at least one of the following shortcomings: (a) They do not provide deterministic error guarantees, resorting to weaker probabilistic error guarantees that are in many cases not acceptable, (b) they allow queries only over a single dataset, thus not supporting the multitude of queries over multiple datasets that appear in practice, such as correlation or cross-correlation and (c) they support relational data in general and thus miss speedup opportunities created by the special nature of sensor data, which are not random but follow a typically smooth underlying phenomenon. To address these problems, we propose PlatoDB; a system that exploits the nature of sensor data to compress them and provide efficient processing of queries over multiple sensor datasets, while providing deterministic error guarantees. PlatoDB achieves the above through a novel architecture that (a) at data import time pre-processes each dataset, creating for it an intermediate hierarchical data structure that provides a hierarchy of summarizations of the dataset together with appropriate error measures and (b) at query processing time leverages the pre-computed data structures to compute an approximate answer and deterministic error guarantees for ad hoc queries even when these combine multiple datasets.

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arXiv2016

Data Compression for Analytics over Large-scale In-memory Column Databases

Data compression schemes have exhibited their importance in column databases by contributing to the high-performance OLAP (Online Analytical Processing) query processing. Existing works mainly concentrate on evaluating compression schemes for disk-resident databases as data is mostly stored on disks. With the continuously decreasing of the price/capacity ratio of main memory, it is the tendencies of the times to reside data in main memory. But the discussion of data compression on in-memory databases is very vague in the literature. In this work, we present an updated discussion about whether it is valuable to use data compression techniques in memory databases. If yes, how should memory databases apply data compression schemes to maximize performance?

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arXiv2016

Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering

Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them. In domains like clothing recommendation, explaining users' preferences requires modeling the visual appearance of the items in question. This makes recommendation especially challenging, due to both the complexity and subtlety of people's 'visual preferences,' as well as the scale and dimensionality of the data and features involved. Ultimately, a successful model should be capable of capturing considerable variance across different categories and styles, while still modeling the commonalities explained by `global' structures in order to combat the sparsity (e.g. cold-start), variability, and scale of real-world datasets. Here, we address these challenges by building such structures to model the visual dimensions across different product categories. With a novel hierarchical embedding architecture, our method accounts for both high-level (colorfulness, darkness, etc.) and subtle (e.g. casualness) visual characteristics simultaneously.

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arXiv2016

Fashionista: A Fashion-aware Graphical System for Exploring Visually Similar Items

To build a fashion recommendation system, we need to help users retrieve fashionable items that are visually similar to a particular query, for reasons ranging from searching alternatives (i.e., substitutes), to generating stylish outfits that are visually consistent, among other applications. In domains like clothing and accessories, such considerations are particularly paramount as the visual appearance of items is a critical feature that guides users' decisions. However, existing systems like Amazon and eBay still rely mainly on keyword search and recommending loosely consistent items (e.g. based on co-purchasing or browsing data), without an interface that makes use of visual information to serve the above needs. In this paper, we attempt to fill this gap by designing and implementing an image-based query system, called Fashionista, which provides a graphical interface to help users efficiently explore those items that are not only visually similar to a given query, but which are also fashionable, as determined by visually-aware recommendation approaches. Methodologically, Fashionista learns a low-dimensional visual space as well as the evolution of fashion trends from large corpora of binary feedback data such as purchase histories of Women's Clothing & Accessories from Amazon, which we use for this demonstration.

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arXiv2016

Fast In-Memory SQL Analytics on Graphs

We study a class of graph analytics SQL queries, which we call relationship queries. Relationship queries are a wide superset of fixed-length graph reachability queries and of tree pattern queries. Intuitively, it discovers target entities that are reachable from source entities specified by the query. It usually also finds aggregated scores, which correspond to the target entities and are calculated by applying aggregation functions on measure attributes, which are found on the target entities, the source entities and the paths from the sources to the targets. We present real-world OLAP scenarios, where efficient relationship queries are needed. However, row stores, column stores and graph databases are unacceptably slow in such OLAP scenarios. We briefly comment on the straightforward extension of relationship queries that allows accessing arbitrary schemas. The GQ-Fast in-memory analytics engine utilizes a bottom-up fully pipelined query execution model running on a novel data organization that combines salient features of column-based organization, indexing and compression. Furthermore, GQ-Fast compiles its query plans into executable C++ source codes. Besides achieving runtime efficiency, GQ-Fast also reduces main memory requirements because, unlike column databases, GQ-Fast selectively allows more dense forms of compression including heavy-weighted compressions, which do not support random access. We used GQ-Fast to accelerate queries for two OLAP dashboards in the biomedical field. It outperforms Postgres by 2-4 orders of magnitude and outperforms MonetDB and Neo4j by 1-3 orders of magnitude when all of them are running on RAM. In addition, it generally saves space due to the appropriate use of compression methods.

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