Private and consortium blockchains have recently raised interest in the blockchain community around facilitating business collaboration. In a business network, multiple participants either (1) work with a centralized authority to maintain a complete and verifiable ledger, or (2) transact in a decentralized manner to maintain an independent shared ledger. A centralized, private blockchain system, such as Amazon QLDB, addresses scenario one with good price-to-performance ratio. The second scenario is commonly backed by a consortium blockchain network, such as Hyperledger Fabric, that requires greater and more complex hardware and software to handle complicated consensus and peer-to-peer communications. The disparate design objectives and resultant principles complicate efforts to handle both scenarios within a single blockchain system. Moreover, the two situations are often mixed together for a user in different business activities, and interchangeable with the evolution of the business collaboration. From the outset, users face a dilemma in choosing which blockchain system type will handle their long-term needs. In this paper, we present a novel Full-Spectrum Blockchain as a Service (FSBaaS) built on top of a high-performance centralized private blockchain runtime called Blockchain Lite and a Hyperledger Fabric blockchain runtime. It handles different scenarios flexibly by providing a unified interface for the two blockchain runtimes from programing model and RESTful APIs to the tenant model. A BaaS tenant can subscribe to the two runtimes simultaneously and migrate data from one to the other as needed. Experimental results contrasted the performance of two blockchain runtimes and the practicality of the proposed BaaS.

Specular reflectance is an important error source in the remote retrieval of leaf pigment content. However, removing this disturbance is challenging because the specular component cannot be directly separated from the leaf reflectance. In this paper, we removed the specular reflectance through polarization measurements at single leaf scale. First, polarization measurements were taken on leaves in the nadir and an oblique viewing direction. Specular reflectance in these two directions was estimated. Second, leaf specular reflectance was subtracted from the total reflectance. Based on the consequent diffuse reflectance, three common types of vegetation indices (VIs), simple ratio (SR), normalized difference (ND), and red edge position (REP) were built to retrieve pigment content. The results show that: 1) specular reflectance interfered with the retrieval. The greater this component, the more serious it caused the disturbance. The retrieval accuracy in forward 35° was much lower than that in the nadir. 2) After specular reflectance removal, the retrieval in both directions was generally improved for the SR and the ND VIs. The improvement in forward 35° was remarkable, with accuracy reaching or approaching the corresponding nadir level. On the two pigments, improvement for chlorophylls was more apparent than for carotenoids. 3) Of all VIs investigated, the REP was insensitive to specular reflectance and had the best resistance capability. Overall, the proposed methods can effectively eliminate specular interference and improve pigment content retrieval. Even under disadvantageous circumstances with strong specular reflectance, the improvement was still reliable.

High-spatial-resolution wind fields derived from synthetic aperture radar (SAR) instruments are crucial to a wide range of applications. However, the spatial scale effect on wind speed retrieval accuracy has seldom been reported. For the purpose of understanding this issue, this letter makes a quality assessment of wind speed retrieval accuracy based on four commonly used C-band geophysical model functions (CMOD4, CMOD-IFR2, CMOD5, and CMOD5.N) at spatial resolutions ranging from 50 m to 50 km using Sentinel-1 interferometric wide (IW) swath mode images. Our results show that the CMOD5 function is the most effective among these functions, owing to a low root-mean-square error (RMSE) of 1.17 m/s and a bias of -0.28 m/s for wind speed retrieval at a spatial resolution of 500 m. It is further observed that the variance of wind speeds retrieved from copolarized SAR images decreases exponentially with the decrease of spatial resolutions. For Sentinel-1 IW mode images, the variance of wind speeds retrieved with CMOD5 decreases rapidly from 50 to 500 m, with a drop in RMSE of 40%, and thereafter levels off. Thus, a spatial resolution of 500 m, with the CMOD5 function, is recommended optimal in this letter, for wind speed retrieval using Sentinnel-1 IW mode data.

In the Internet of Things (IoT) era, with ubiquitous remote sensing devices and other diverse data sources, nearly everything can forward voluminous data continuously, in real-time, which drives demand to perform real-time analytics on uninterrupted IoT data flows. The typical resultant approach is a cloud-centered architecture providing an analytic service for real-time IoT data processing. However, a cloud-centered IoT analytic service cannot guarantee real-time responsiveness has a high-fee pay-as-you-go business model, and opens data privacy concerns. Hence, it becomes rational to shift analytic workloads to the edge and provide a management service for edge analysis. Existing work on providing edge analytics as a service encountered challenges such as lacking a lightweight way to compose IoT applications based on multiple service providers, lacking a flexible and unified way to define domain-specific analytic logic, and maintaining efficiency when processing data on a resource-limited edge. This paper presents EAaaS, a scalable analytic service for enabling real-time edge analytics in IoT scenarios. In this work, we propose a unified rule-based analytic model to ease user's programming efforts in specifying rule-based analytic logic. Moreover, we also designed and implemented a high performance edge engine to apply rule-based analytic on incoming device data streams. To simplify the access to EAaaS service, a group of RESTful web interfaces is also designed for edge analytic management on cloud and flexible composition with external services. EAaaS is implemented as a part of IBM Watson IoT Platform, which is a cloud service for elementary IoT application development on IBM Bluemix cloud announced by IBM recently. We have conducted proof of correctness (PoC) of EAaaS with customers from boat racing in the U.S. and collected valuable feedback from customers for further enhancement of EAaaS's flexibility and usability.

The emergence of heterogeneous big data in the last decade calls for a hybrid data service that can manage all different kinds of data, including relational data, JSON data, and text data in a unified way. Among them, text data play an important role in many fields such as Internet-of-Things, biology, social network, and etc. For example, a smart meter application detecting the anomaly of the electricity use might want to link each anomaly of a certain area to a meaningful social event mined from the news in plain text. As a result, text data services have raised more and more attentions by the research community. Most of such services are implemented based on a content management system such as ElasticSearch and Solr. However, we found that the mere content management capabilities are not enough. On one hand, text data query often requires join operations to relational data or JSON data in an existing DBMS. On the other hand, users often have to pull the big text data out to an independent system or service for further text analytics. In this paper, we present our Text-DataBase-as-a-Service (TDBaaS), which is built on top of the Hybrid Data Service (HDS) from IBM Research. The TDBaaS is designed to manage the text data together with relational data and JSON data in a single service. Basic text analytics can be conducted directly inside the database in the form of general SQLs. Moreover, the extensible framework allows the service to have abundant text analytic capabilities with high performance. As a case study, we investigate in the implementation of the top-k word algorithm, and show how the common computations are shared across different tenants in the TDBaaS. The experimental results demonstrate the high performance of the TDBaaS on both text data management and text data analytics.

Nowadays vast amounts of data are being produced in continuous ways. They may come from sensors, smart meters, application logs, monitoring software etc. The data need to be processed in realtime to gain actionable insights. Services like smart grid load balancing, cloud platform maintenance, can be carried out in an efficient way. Stream processing is the programming paradigm that answers such demand. When talking about stream processing, we can easily recall several famous open-source software frameworks such as Spark Streaming, Samza, Flink and Storm. Although they provide distributed, robust, low-latency stream processing engines, it's still difficult for an end user to set up a usable stream processing application from scratch. Firstly, users are required to write code to define their business related stream processing logic. Secondly, the submission and update of the stream processing logic require service restart, therefore it may lead to service unavailability for minutes. Thirdly, extra operation effort are required for handling scaling and failover issues. In this paper, we present RTA, a released research service on realtime data processing. The RTA service fills the gap between the stream processing requester and the existing software stacks. It offers a SQL-like stream query language for defining stream processing logic definition over streaming data. It allows users easily define their stream processing logic without programming. In RTA service, stream processing logic is also treated as a type of input, which enables online logic update without service downtime. The RTA service also provides scalability, high availability and resource isolation for serving multiple tenants. In this paper, we also provide a comprehensive evaluation of our service through a case study.

Publish/subscribe (pub/sub) systems are widely used in numerous Internet-Of-Things (IoT) applications such as environment monitoring, supply chain tracing, healthcare, and vehicle networks. In these applications, publishers (e.g. Smart devices, sensors) are continuously generating large volume of data with an extremely high throughput, whereas subscribers are only interested in a small portion of the data. Recently, content-based subscription systems have raised more and more attentions by the researchers where subscribers can specify rules on the content of messages that are composed of many attributes. For example, in traffic monitoring, an operator is only interested in the data within a specified area defined by constraints on latitude and longitude instead of the whole map. In this paper, we present COSS, the first Content-based Subscription Service for IoT with natural multi-tenant support and easy-to-use REST APIs. Moreover, we investigate in the problem of Balanced Rule Engine Partitioning for content-based subscription under the Tenant-Message-Rule (TMR) model. We show the NP-hardness of the problem and design a heuristics to enable COSS to adaptively adjust the message distribution according to the workload history, and to scale on both the high data throughput of IoT workloads and multi-tenant. Extensive experiments show that COSS offers high performance and scalability for content-based subscription in terms of the number of tenants, and the data throughput of the messages.

In recent years, time series data are everywhere across different industry, which creates a huge demand on time series data analysis, such as pattern search. Meanwhile, it is increasingly realized that only when pattern search results together with information from relational tables could be used in a programming-free way, can they perform analysis on time series conveniently. Hence, casual users highly demand that queries involving pattern search could be performed via SQLs. However, existing database products supporting time series data type lack the capability to perform pattern searches on time series data. This paper presents SearchonTS, an extendable framework for in-database pattern search on time series data. It provides a series of interfaces so that time series index and pattern search can be added and performed in a uniformed and query optimized manner. SearchonTS is implemented as an extension on Informix, which is a database product in IBM software product series. It targets a future release of IBM Informix. We have implemented index-based pattern search for Euclid Distance(ED) via SearhonTS to demonstrate its usability for developers. And real scenario is also provided to show SQL involving pattern search so that users can have a more clear experience of the convenience.

In recent years, the evolving of IoT (Internet of Things) has resulted in the deployment of massive numbers of sensors in various fields, such as the Energy and Utility (E&U) industry. These sensors are continuously producing a huge amount of time series data, which creates a correspondingly huge demand for time series data analysis, such as pattern searching. However, analysis on time series data is currently implemented as custom applications, a strategy that suffers from low efficiency and high maintenance costs. Hence there is a need to provide a service for analysis on time series data that reduces maintenance costs and enhances query efficiency. Existing time series data management services lack the capability to perform pattern searches on the massive amount of data from sensors. This paper presents Time Series analytics as a Service (TSaaaS), a scalable analytic service for time series data in IoT scenarios. We designed pattern searching in TSaaaS that can support efficient and effective searching on truly massive amounts of time series data with very little overhead on the IoT system. To simplify access to the TSaaaS, we created a group of RESTful web interfaces. TSaaaS is implemented as an extension to the Time Series Database service in the IBM cloud platform offering (Codename: BlueMix), which is a new product to accelerate IoT application development. TSaaaS targets a future release of the Time Series Database service. We have conducted proofs of concept (PoC) of TSaaaS with real-world customers from power meter management and bridge monitoring in China. The pilot results and other experiments show that for a selection of pattern cases provided by customers, pattern searches via our service are 10-100 times faster than the existing techniques, while the additional storage cost for the service provider accounts for only about 0.4% of original time series data.

In the era of the Internet of Things (IoT), increasing numbers of applications face the challenge of using current data management systems to manage the massive volume of operational data gener-ated by sensors and devices. Databases based on time series data model, like PI Server, are developed to handle such data with operational technology (OT) characteristics (high volume, long lifecycle, and simple format). However, while achieving excellent write performance, these database systems provide limited query capabilities. In this paper, we present the next-generation Opera-tional Data Historian (ODH) system that is based on the IBM© Informix© system architecture. The system combines the write advantages of the existing time series databases and the ability to run complex queries in SQL. We demonstrate the high efficiency of our system for both writing and querying data with a variety of case studies in the industries of Energy and Utilities and con-nected vehicles. In addition, we present the first benchmark, IoT-X, to evaluate technologies on operational data management for IoT.