This paper presents a simplified direct modulation prediction approach for modular multilevel converters (MMC). By using the Euler approximation, the grid-side and circulating currents are firstly predicted to obtain the values in the next switching cycle. Then the modulations are directly calculated based on the established mathematical models for the six-arms of MMC. Subsequently, the capacitor voltage balancing control is designed according to the total on-state submodules (SMs) of each arm, aiming at reducing the computational burden. Finally, proper pulse width modulation (PWM) can be obtained based on proposed control strategy. The effectiveness of the proposed method is verified by both the simulation and experimental results. Besides, compared with the conventional method, the superiority of the proposed approach is increasing with the voltage levels to lessen the computational burden.

Knowledge bases that summarize web information in RDF triples deliver many benefits, including providing access to encyclopedic knowledge via SPARQL queries and end-user interfaces. As the real world evolves, the knowledge base is updated and the evolution history of entities and their properties becomes of great interest to users. Thus, users need query tools of comparable power and usability to explore such evolution histories or flash-back to the past. An integrated system that supports user-friendly queries and efficient query evaluation on the history of knowledge bases is required. In this paper, we introduce (i) SPARQL T , a temporal extension of SPARQL that expresses powerful structured queries on temporal RDF graphs, (ii) an efficient in-memory query engine that takes advantage of compressed multiversion B+ trees to achieve fast evaluation of SPARQL T queries, and (iii) a query optimizer that improves selectivity estimation of temporal queries and generates efficient join orders using the statistics of temporal RDF graphs. The performance and scalability of our system are validated by extensive experiments on real world datasets, which shows significant performance improvement comparing with other approaches.

Spatiotemporal data are often expressed in terms of granularities to indicate the measurement units of the data. A granularity system usually consists of a set of granularities that share a “common refined granularity” (CRG) to enable granular comparison and data conversion within the system. However, if data from multiple granularity systems needs to be used in a unified application, it is necessary to extend the data conversion and comparison within a granularity system to those for multiple granularity systems. This paper proposes a formal framework to enable such an extension. The framework involves essentially some preconditions and properties for verifying the existence of a CRG and unifying conversions of incongruous semantics, and supports the approach to integrate multiple systems into one so as to process granular interoperation across systems just like in a single system. Quantification of uncertainty in granularity conversion is also considered to improve the precision of granular comparison.

Approximate Query Processing (AQP) based on sampling is critical for supporting timely and cost-effective analytics over big data. To be applied successfully, AQP must be accompanied by reliable estimates on the quality of sample-produced approximate answers; the two main techniques used in the past for this purpose are (i) closed-form analytic error estimation, and (ii) the bootstrap method. Approach (i) is extremely efficient but lacks generality, whereas (ii) is general but suffers from high computational overhead. Our recently introduced Analytical Bootstrap method combines the strengths of both approaches and provides the basis for our ABS system, which will be demonstrated at the conference. The ABS system models bootstrap by a probabilistic relational model, and extends relational algebra with operations on probabilistic relations to predict the distributions of the AQP results. Thus, ABS entails a very fast computation of bootstrap-based quality measures for a general class of SQL queries, which is several orders of magnitude faster than the standard simulation-based bootstrap. In this demo, we will demonstrate the generality, automaticity, and ease of use of the ABS system, and its superior performance over the traditional approaches described above.

Sampling is one of the most commonly used techniques in Approximate Query Processing (AQP)-an area of research that is now made more critical by the need for timely and cost-effective analytics over "Big Data". Assessing the quality (i.e., estimating the error) of approximate answers is essential for meaningful AQP, and the two main approaches used in the past to address this problem are based on either (i) analytic error quantification or (ii) the bootstrap method. The first approach is extremely efficient but lacks generality, whereas the second is quite general but suffers from its high computational overhead. In this paper, we introduce a probabilistic relational model for the bootstrap process, along with rigorous semantics and a unified error model, which bridges the gap between these two traditional approaches. Based on our probabilistic framework, we develop efficient algorithms to predict the distribution of the approximation results. These enable the computation of any bootstrap-based quality measure for a large class of SQL queries via a single-round evaluation of a slightly modified query. Extensive experiments on both synthetic and real-world datasets show that our method has superior prediction accuracy for bootstrap-based quality measures, and is several orders of magnitude faster than bootstrap.

In this paper, we study the problem of mining for frequent trajectories, which is crucial in many application scenarios, such as vehicle traffic management, hand-off in cellular networks, supply chain management. We approach this problem as that of mining for frequent sequential patterns. Our approach consists of a partitioning strategy for incoming streams of trajectories in order to reduce the trajectory size and represent trajectories as strings. We mine frequent trajectories using a sliding windows approach combined with a counting algorithm that allows us to promptly update the frequency of patterns. In order to make counting really efficient, we represent frequent trajectories by prime numbers, whereby the Chinese reminder theorem can then be used to expedite the computation.

Crowd selection is essential to crowd sourcing applications, since choosing the right workers with particular expertise to carry out crowdsourced tasks is extremely important. The central problem is simple but tricky: given a crowdsourced task, who are the most knowledgable users to ask? In this demo, we show our framework that tackles the problem of crowdsourced task assignment on Twitter according to the social activities of its users. Since user profiles on Twitter do not reveal user interests and skills, we transfer the knowledge from categorized Yahoo! Answers datasets for learning user expertise. Then, we select the right crowd for certain tasks based on user expertise. We study the effectiveness of our system using extensive user evaluation. We further engage the attendees to participate a game called--Whom to Ask on Twitter?. This helps understand our ideas in an interactive manner. Our crowd selection can be accessed by the following url http://webproject2.cse.ust.hk:8034/tcrowd/.

Knowledge bases and structured summaries are playing a crucial role in many applications, such as text summarization, question answering, essay grading, and semantic search. Although, many systems (e.g., DBpedia and YaGo2) provide massive knowledge bases of such summaries, they all suffer from incompleteness, inconsistencies, and inaccuracies. These problems can be addressed and much improved by combining and integrating different knowledge bases, but their very large sizes and their reliance on different terminologies and ontologies make the task very difficult. In this demo, we will demonstrate a system that is achieving good success on this task by: i) employing available interlinks in the current knowledge bases (e.g. externalLink and redirect links in DBpedia) to combine information on individual entities, and ii) using widely available text corpora (e.g. Wikipedia) and our IBminer text-mining system, to generate and verify structured information, and reconcile terminologies across different knowledge bases. We will also demonstrate two tools designed to support the integration process in close collaboration with IBminer. The first is the InfoBox Knowledge-Base Browser (IBKB) which provides structured summaries and their provenance, and the second is the InfoBox Editor (IBE), which is designed to suggest relevant attributes for a user-specified subject, whereby the user can easily improve the knowledge base without requiring any knowledge about the internal terminology of individual systems.