Kia Teymourian

Kia Teymourian
Boston University | BU · Department of Computer Science, Metropolitan College

PhD Computer Science (Free University Berlin)

About

41
Publications
8,677
Reads
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365
Citations
Citations since 2016
9 Research Items
191 Citations
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2016201720182019202020212022010203040
2016201720182019202020212022010203040
Additional affiliations
May 2007 - January 2015
Freie Universität Berlin
Position
  • Research Associate

Publications

Publications (41)
Article
Full-text available
Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper...
Conference Paper
Full-text available
In many robotic applications, LiDAR (Light Detection and Ranging) scanner is used to gather data about the environment. Applications like autonomous vehicles require real-time processing of LiDAR point cloud data with high accuracy. We describe in this paper, our implementation for DEBS 2019 Grand Challenge for an object recognition system from hig...
Conference Paper
This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. \emphIn the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computa...
Article
Full-text available
This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computations...
Conference Paper
Full-text available
Many cloud-based data management and analytics systems support complex objects. Dataflow platforms such as Spark and Flink allow programmers to manipulate sets consisting of objects from a host programming language (often Java). Document databases such as MongoDB make use of hierarchical interchange formats---most popularly JSON---which embody a da...
Conference Paper
Full-text available
Real-time analytics over data streams are crucial for a wide range of use cases in industry and research. Today's sensor systems can produce high throughput data streams that have to be analyzed in real-time. One important analytic task is anomaly or outlier detection from the streaming data. In many industry applications, sensing devices produce a...
Conference Paper
Full-text available
Detection of stateful complex event patterns using parallel programming features is a challenging task because of statefulness of event detection operators. Parallelization of event detection tasks needs to be implemented in a way that keeps track of state changes by new arriving events. In this paper, we describe our implementation for a customize...
Chapter
In today’s business processes, complex issues happen in real time. One of the crucial success factors is the timely delivery of information to trigger reactions to potential challenges and opportunities. Huge amounts of an organization’s real-time data that flow into different systems as data stream need to be filtered, mapped, enriched, and proces...
Conference Paper
Full-text available
Information overload on news data is a known problem these days. People and organizations have an increasing demand for extraction of relevant information from massive amounts of news data arriving in real-time as news streams. In this paper, we present a novel approach for real-time extraction of news, based on user specifications and by using bac...
Conference Paper
Detecting occurrences of complex events in an event stream requires designing queries that describe real-world situations. However, specifying complex event patterns is a challenging task that requires domain and system specific knowledge. Novel approaches are required that automatically identify patterns of potential interest in a heavy flow of ev...
Conference Paper
Full-text available
We describe an approach for a custom complex event processing engine using Message Passing Interface (MPI) in C++ programming language. Our approach utilizes a multi-processor infrastructure and distributes its load on multiple processes, expecting each process to run on one processor. A dispatching process receives events and distributes them on s...
Conference Paper
DBpedia Live enables access to structured data extracted from Wikipedia in real-time. A data stream that is generated from Wikipedia changes is instantly loaded in the DBpedia RDF store. Applications can benefit by subscribing to the RDF update stream and receive continuous results from DBpedia. Providing a continuous update stream of changes to su...
Conference Paper
Full-text available
Background knowledge about the application domain can be integrated in event processing in order to improve complex event processing systems. The idea of semantic enrichment is to incorporate background knowledge into events, thereby generating new enriched events, which in the next step can be then better understood and processed by event processi...
Conference Paper
Full-text available
Real-time microblogging messages are an interesting data source for the realization of early warning systems that track the outbreaks of epidemic diseases like seasonal or pandemic influenza. Microblogging monitoring systems might be able to detect disease outbreaks in communities faster than the traditional public health services. The realization...
Conference Paper
Full-text available
Feature-based sentiment analysis can be realized on different types of object features. Some of these features might be about technical aspects of the objects and some others might be application-oriented features. The application-oriented features are more abstract features and can be of interest to the broad number of people than only the technic...
Conference Paper
Full-text available
RuleML is a family of XML languages whose modular system of schemas permits high-precision (Web) rule interchange. The family's top-level distinction is deliberation rules vs. reaction rules. In this paper we address the Reaction RuleML subfamily of RuleML and survey related work. Reaction RuleML is a standardized rule markup/serialization language...
Article
Full-text available
Reaction RuleML is one of the two major subfamilies of RuleML and acts as an interchange format for reactive rules and rule-based event-processing languages. Exemplified with a recent instantiation of Rule Responder, a rule-based inference agent middleware, we demonstrate the event messaging features of Reaction RuleML, which supports loosely-coupl...
Article
Full-text available
Usage of background knowledge about events and their relations to other concepts in the application domain can improve the expressiveness and flexibility of complex event processing systems. Huge amounts of domain background knowledge stored in external knowledge bases can be used in combination with event processing in order to achieve more knowle...
Article
Full-text available
Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing. In this paper, we describe a system for knowledge-based event detection of complex stock market events based on available background knowledge about stock market companies. Our system profits from...
Conference Paper
Usage of domain background knowledge about sensor data can improve the expressiveness and flexibility of event processing in sensor network applications. Huge amount of domain background knowledge stored in external knowledge bases can be processed in combination with sensor data stream in order to achieve more knowledgeable event processing. In th...
Article
Usage of background knowledge about events and their relations to other concepts in the application domain can improve the expressiveness and flexibility of complex event processing systems. Huge amounts of domain background knowledge stored in external knowledge bases can be used in combination with event processing in order to achieve more knowle...
Conference Paper
Reusing existing Semantic Web ontologies is necessary to avoid heterogeneity as well as redundant modeling efforts, because ontology engineering is a time-consuming and cost-intensive task. In order to decide whether a candidate ontology comprises the right concepts, an analysis process is necessary to understand the conceptual model of the ontolog...
Conference Paper
Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing. In this paper, we describe a system for knowledge-based event detection of complex stock market events based on available background knowledge about stock market companies. Our system profits from...
Conference Paper
One of the critical success factors of event-driven systems is the capability of detecting complex events from simple and ordinary event notifications. Complex events which trigger or terminate actionable situations can be inferred from large event clouds or event streams based on their event instance sequence, their syntax and semantics. Using sem...
Conference Paper
Full-text available
Event-driven systems are highly depending on the quality of detection and processing of events. Many of complex real-world events cannot be processed by the existing event processing systems because they are too complex to be understood and processed by the systems. Complex events can be inferred from raw primitive events based on their incoming se...
Conference Paper
Full-text available
Traditional approaches for data storage and analysis are facing their limits when handling the enormous data amounts of today's applications. We believe that a radical departure from contemporary architectures of stores is necessary to satisfy that central scalability requirement. One of the most promising new schools of thought in system design ar...
Conference Paper
Full-text available
Event-driven systems are highly depending on the quality of detection and processing of events. Many of complex real-world events cannot be processed by the existing event processing systems because they are too complex to be understood and processed by the systems. Complex events can be inferred from raw primitive events based on their incoming se...
Article
Traditional approaches for semantic storage and analysis are facing their limits on the handling of enormous data amounts of today's applications. We believe that a more radical departure from contemporary architectures of stores is necessary to satisfy that central scalability requirement. One of the most promising new schools of thought in system...
Conference Paper
Full-text available
One of the critical success factors of event-driven systems is the capability of detecting complex events from simple and ordinary event notifications. Complex events which trigger or terminate actionable situations can be inferred from large event clouds or event streams based on their event instance sequence, their syntax and semantics. Using sem...
Conference Paper
The developments and successes of the Semantic Web research community in building standards and tools for semantic technologies such as formalized vocabularies/ontologies and declarative rules are opening novel research and application areas. One of these promising application areas is semantic event processing. Semantic models of events can improv...
Article
Full-text available
While WS-BPEL addresses the industry's need for rich and standard ser-vice orchestration semantics it provide only limited expressiveness to describe (busi-ness) decision logic and conditional reaction logic. In this paper we propose a heteroge-nous service-oriented integration of rules into BPEL to describe rule-based business processes and implem...
Conference Paper
Triple Space Computing is a new middleware paradigm [11,13] based on semantics and tuplespaces which can be used for the coordination of Semantic Web clients and services. To achieve scalability of Triple Space infrastructure distribution of triplespaces is necessary. A major problem within massively distributed triplespaces is to find the best sui...
Conference Paper
Full-text available
Knowledge in the form of semantic data is becoming more and more ubiquitous, and the need for scalable, dynamic systems to support collaborative work with such distributed, heterogeneous knowledge arises. We extend the “data in the cloud” approach that is emerging today to “knowledge in the cloud”, with support for handling semantic information, or...
Conference Paper
The semantic Web and Web services have emerged as a new paradigm for knowledge-based applications, both human and machine controlled. The coordination of semantic clients or services is necessary to achieve goals only possible from the combination of knowledge based activities. Triple space computing is a new coordination paradigm based on semantic...
Article
Triple Space Middleware is a highly scalable, semantically enhanced platform for automatic machine-based communication on the basis of Web services, Semantic Web technologies and the coordination medium of Tuple Spaces. For this, we specify and prototypically implement a Triple Space system that innovatively combines these three research areas. As...
Article
Full-text available
The amount of data handled by semantic applications is expected to increase over a level manageable by available storage systems. Distributed se-mantic storage solutions are a promising way to increase storage capacity, but current approaches often rely on static network structures. We are in the process of developing a Self-Organized Semantic Stor...

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Projects

Projects (5)
Archived project
Evaluate event-based systems for real-time analytics over high velocity and high volume data streams.
Archived project
Corporate Smart Content is semantically enriched content generated in or acquired by enterprises. It is the basis for "smart" content-centric applications that rely on apt smart content delivery. Use cases for Corporate Smart Content are situation- and context-aware in-house use of proprietary content in knowledge intensive enterprise processes and end user-tailored content delivery (e.g. personalized news). In collaboration with four Berlin-based industrial partners, the SCE research team around Prof. Dr. Adrian Paschke focuses on the process chain that is necessary to produce semantically enriched Corporate Smart Content.