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The rapid advancement of sensor technology presents novel challenges in the efficient management of large scale time series data. In this demo, we demonstrate a time series data management module named GaussTS in database, which provides four key ...

The importance of data cleaning systems has continuously grown in recent years. Especially for real-time streaming applications, it is crucial, to identify and possibly remove anomalies in the data on the fly before further processing. The main challenge ...

Object detectors are core components of multimodal models, enabling them to locate the region of interest in images which are then used to solve many multimodal tasks. Among the many extant object detectors, the Bottom-Up Faster R-CNN [39] (BUA) ...

Data preprocessing is thought of as one of the most important steps in data analytics. This is especially true for the field of Supply Chain Management (SCM), in which the handling of huge data sets is the norm. Data preprocessing consists of multiple ...

The presence of many different species of bees in nature increases the diversity of ecosystems. However, when the original ecology of bees is destroyed, related bees will migrate. Nevertheless, large-scale migratory behavior may seriously affect local ...

With the continuous development of power business, the demand of data interaction between internal and external network is becoming more and more frequent, resulting in a large number of Structured Query Language (SQL) interactive instructions. Aiming ...

South Africa (S.A.) is currently facing economic and social challenges that could benefit from the implementation of international smart city guidelines. Crucial to transforming a city into a smart city is the collection and access to reliable data. One ...

It is a fact that there are tons of GPS traces generated every minute by the millions of in-road vehicles over the world. Naturally, those traces contain imprecise readings, and most of the time they include noise and outliers. Therefore, there is a ...

GPS data is noisy by nature. A typical location-based service would start by filtering out the noise from the raw GPS points that are generated by moving objects. Once the locations of the objects are identified, the location-based service is provided. ...

In the information era, data is crucial in decision making. Most data sets contain impurities that need to be weeded out before any meaningful decision can be made from the data. Hence, data cleaning is essential and often takes more than 80 percent ...

At present, the modern manufacturing and management concepts such as digitalization, networking and intellectualization have been popularized in the industry, and the degree of industrial automation and information has been improved unprecedentedly. ...

This article contends that in the booming era of information, analysing users' navigation behaviour is an important task. User identification is considered as one of the important and challenging tasks in the data preprocessing phase of the Web usage ...

Data cleansing can be considered to be an activity that is performed on the data sets of the data warehouse. The cleansing is done in order to enhance and collectively maintain data consistency and quality. The quality of data has a strong impact on a ...

As the Vrije Universiteit Brussel switched from an in-house built CRIS to Pure, a large number of data quality issues were discovered. In order to solve these, a large-scale data quality assessment and improvement program was started. The assessment ...

Dealing with increasing amounts of data creates the need to deal with redundant, inconsistent and/or complementary repositories which may be different in their data models and/or in their schema. Current data cleaning techniques developed to tackle data ...

A critical task in data cleaning and integration is the identification of duplicate records representing the same real-world entity. A popular approach to duplicate identification employs similarity join to find pairs of similar records followed by a ...

This research describes a general method to automatically clean organizational and business names variants within large databases, such as: patent databases, bibliographic databases, databases in business information systems, or any other database ...

The organizations' demand to integrate several heterogeneous data sources and an ever-increasing volume of data is revealing the presence of quality problems in data. Currently, most of the data cleaning approaches (for detection and correction of data ...

Data is a valuable resource. The proper use of high-quality data can help make better predictions, analysis and decisions. Poor-quality data is detrimental to data analytics. Data from different sources may provide the same entities, but different ...

It is widely accepted that proper data publishing is difficult. The majority of Linked Open Data (LOD) does not meet even a core set of data publishing guidelines. Moreover, datasets that are clean at creation, can get stains over time. As a ...