Emerging domains, such as sensor-driven smart spaces and social media analytics, require incoming data to be enriched prior to its use. Enrichment often consists of machine learning (ML) functions that are too expensive/infeasible to execute at ingestion. We develop a strategy entitled Just-in-time ENrichmeNt in quERy Processing (JENNER) to support interactive analytics over data as soon as it arrives for such application context. JENNER exploits the inherent tradeoffs of cost and quality often displayed by the ML functions to progressively improve query answers during query execution. We describe how JENNER works for a large class of SPJ and aggregation queries that form the bulk of data analytics workload. Our experimental results on real datasets (IoT and Tweet) show that JENNER achieves progressive answers performing significantly better than the naive strategies of achieving progressive computation.

Social media analysis over blogs (such as tweets) often requires determining top-k mentions of a certain category (e.g., movies) in a collection (e.g., tweets collected over a given day). Such queries require entity linking (EL) function to be executed that is often expensive. We propose TQEL, a framework that minimizes the joint cost of EL calls and top-k query processing. The paper presents two variants - TQEL-exact and TQEL-approximate that retrieve the exact / approximate top-k results. TQEL-approximate, using a weaker stopping condition, achieves significantly improved performance (with the fraction of the cost of TQEL-exact) while providing strong probabilistic guarantees (over 2 orders of magnitude lower EL calls with 95% confidence threshold compared to TQEL-exact). TQEL-exact itself is orders of magnitude better compared to a naive approach that calls EL functions on the entire dataset.

Entity resolution (ER) is the process of identifying which entities in a dataset refer to the same real-world object. In relational ER, the dataset consists of multiple entity-sets and relationships among them. Such relationships cause the resolution of some entities to influence the resolution of other entities. For instance, consider a relational dataset that consists of a set of research paper entities and a set of venue entities. In such a dataset, deciding that two research papers are the same may trigger the fact that their venues are also the same. This article proposes a progressive approach to relational ER, named ProgressER, that aims to produce the highest quality result given a constraint on the resolution budget, specified by the user. Such a progressive approach is useful for many emerging analytical applications that require low latency response (and thus cannot tolerate delays caused by cleaning the entire dataset) and/or in situations where the underlying resources are constrained or costly to use. To maximize the quality of the result, ProgressER follows an adaptive strategy that periodically monitors and reassesses the resolution progress to determine which parts of the dataset should be resolved next and how they should be resolved. More specifically, ProgressER divides the input budget into several resolution windows and analyzes the resolution progress at the beginning of each window to generate a resolution plan for the current window. A resolution plan specifies which blocks of entities and which entity pairs within blocks need to be resolved during the plan execution phase of that window. In addition, ProgressER specifies, for each identified pair of entities, the order in which the similarity functions should be applied on the pair. Such an order plays a significant role in reducing the overall cost because applying the first few functions in this order might be sufficient to resolve the pair. The empirical evaluation of ProgressER demonstrates its significant advantage in terms of progressiveness over the traditional ER techniques for the given problem settings.

Twitter provides a strictly limited API that makes it difficult for a simple search using pre-defined textual patterns to provide satisfying coverage of the topic of interest. This paper discusses a tweet acquisition system, that queries Twitter API using a set of key phrases, then analyzes the retrieved tweets. In order to achieve better coverage of the searched topic, the system employs an adaptive query generation mechanism that iteratively enriches the set of textual relevant patterns based on the previously collected tweets using an explore-exploit strategy. The paper also demonstrates an application called Topic Follow-up on Twitter (TFT) that is built on top of the acquisition system and aims at linking tweets with online articles. It first extracts a set of key phrases from the submitted news article and then utilizes the acquisition and analysis components of the system. Using this application, we will show how the adaptive searching mechanism of the tweet acquisition system improves the coverage of the topic of interest. Video: http://bit.ly/2kqkikB.

Entity resolution (ER) is the process of identifying which entities in a dataset represent the same real-world object. This paper proposes a progressive approach to ER using MapReduce. In contrast to traditional ER, progressive ER aims to resolve the dataset such that the rate at which the data quality improves is maximized. Such a progressive approach is useful for many emerging analytical applications that require low latency response and/or in situations where the underlying resources are constrained or costly to use. Experiments with real-world datasets demonstrate the ability of our approach to generate high-quality results using limited amounts of resolution cost.

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B + -tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.

This paper proposes a progressive approach to entity resolution (ER) that allows users to explore a trade-off between the resolution cost and the achieved quality of the resolved data. In particular, our approach aims to produce the highest quality result given a constraint on the resolution budget, specified by the user. Our proposed method monitors and dynamically reassesses the resolution progress to determine which parts of the data should be resolved next and how they should be resolved. The comprehensive empirical evaluation of the proposed approach demonstrates its significant advantage in terms of efficiency over the traditional ER techniques for the given problem settings.

At UC Irvine, we are building a next generation parallel database system, called ASTERIX, as our approach to addressing today's "Big Data" management challenges. ASTERIX aims to combine time-tested principles from parallel database systems with those of the Web-scale computing community, such as fault tolerance for long running jobs. In this demo, we present a whirlwind tour of ASTERIX, highlighting a few of its key features. We will demonstrate examples of our data definition language to model semi-structured data, and examples of interesting queries using our declarative query language. In particular, we will show the capabilities of ASTERIX for answering geo-spatial queries and fuzzy queries, as well as ASTERIX' data feed construct for continuously ingesting data.