Event Processing and Stream Reasoning with ETALIS

This thesis presents the ETALIS Language for Events (ELE), a declarative rule-based language for Event Processing (EP) and Stream Reasoning (SR). ELE features a well-defined semantics, and provides strong event processing and reasoning capabilities. In this work we present ELE and show how its EP and SR capabilities have the potential to provide powerful real time intelligence. We provide a prototype implementation of the language, and present evaluation results for a few implemented scenarios

TinyReptile: TinyML with Federated Meta-Learning

Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications can benefit from aggregating their knowledge. Federated learning (FL) enables decentralized agents to jointly learn a global model without sharing sensitive local data. However, a common global model may not work for all devices due to the complexity of the actual deployment environment and the heterogeneity of the data available on each device. In addition, the deployment of TinyML hardware has significant computational and communication constraints, which traditional ML fails to address. Considering these challenges, we propose TinyReptile, a simple but efficient algorithm inspired by meta-learning and online learning, to collaboratively learn a solid initialization for a neural network (NN) across tiny devices that can be quickly adapted to a new device with respect to its data. We demonstrate TinyReptile on Raspberry Pi 4 and Cortex-M4 MCU with only 256-KB RAM. The evaluations on various TinyML use cases confirm a resource reduction and training time saving by at least two factors compared with baseline algorithms with comparable performance.Comment: Accepted by The International Joint Conference on Neural Network (IJCNN) 202

Uzorkovanje neoplastičnog tkiva pasa i prateća dokumentacija

Broj onkoloških slučajeva ima višegodišnji trend rasta, kako kod ljudi, tako i kod životinja, pre svega pasa. Prevencija ili dijagnostika neoplazmi u ranoj fazi bolesti u velikoj meri zavise od histološkog pre- gleda uzorka i obično su jedan od početnih koraka u postavljanju di- jagnoze. Postavljanje tačne dijagnoze, prema savremenim kriterijumi- ma za histopatološku dijagnostiku, predstavlja osnovni preduslov za sprovođenje adekvatnih terapijskih protokola, proširenje i unapređe- nje mogućnosti lečenja onkoloških pacijenata i produžavanje njiho- vog života. Histopatološka dijagnoza, pored klasifikacije i određivanja gradusa tumora, pruža kliničarima i informacije o tome da li je eksci- zija bila adekvatna, da li su margine slobodne od tumorskih ćelija, da li je tumor infiltrovao okolno tkivo i da li je započeo metastazu prodi- ranjem u krvne i/ili limfne sudove, kao i da li postoje metastaze u ko- respondentnim limfnim čvorovima, ukoliko su i oni promenjeni. Važan preduslov za postavljanje pouzdane histopatološke dijagnoze je uzor- kovanje neoplastičnog tkiva, njegovo fiksiranje i transport do laborato- rije i jasno popunjena prateća dokumentacija. Odabir tkivnog uzorka sa neadekvatne lokacije, uzorkovanje suviše velikog ili suviše malog uzorka, greške u izboru fiksativa i procesu fiksiranja, kao i nepravilno popunjen uput za ispitivanje, mogu u velikoj meri da utiču na obradu tkiva, kvalitet preparata i dobijene rezultate. Greške načinjene tokom uzorkovanja tkiva najčešće dovode do trajnih posledica i u kasnijim fa- zama obrade i naknadno teško mogu da budu otklonjene

Retractable complex event processing and stream reasoning

Abstract. Complex Event Processing (CEP) deals with processing of continuously arriving events with the goal of identifying meaningful patterns (complex events). In existing stream database approaches, CEP is manly concerned by temporal relations between events. This paper advocates for a knowledge-rich CEP with Stream Reasoning capabilities. Secondly, we address the problem of revision in event processing. Events are often assumed to be immutable and therefore always correct. Revision in event processing deals with the circumstance that certain events may be revoked. This necessitates to reconsider complex events which might have been computed based on the original, flawy history as soon as part of that history is corrected. In this paper, we present a novel approach for knowledge-based CEP and Stream Reasoning, including revisions of events too. We present a rule-based language for pattern matching over event streams with a precise syntax and the declarative semantics. We devise an execution model for the proposed formalism, and provide a prototype implementation. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approach

SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT

Internet of Things (IoT) is transforming the industry by bridging the gap between Information Technology (IT) and Operational Technology (OT). Machines are being integrated with connected sensors and managed by intelligent analytics applications, accelerating digital transformation and business operations. Bringing Machine Learning (ML) to industrial devices is an advancement aiming to promote the convergence of IT and OT. However, developing an ML application in industrial IoT (IIoT) presents various challenges, including hardware heterogeneity, non-standardized representations of ML models, device and ML model compatibility issues, and slow application development. Successful deployment in this area requires a deep understanding of hardware, algorithms, software tools, and applications. Therefore, this paper presents a framework called Semantic Low-Code Engineering for ML Applications (SeLoC-ML), built on a low-code platform to support the rapid development of ML applications in IIoT by leveraging Semantic Web technologies. SeLoC-ML enables non-experts to easily model, discover, reuse, and matchmake ML models and devices at scale. The project code can be automatically generated for deployment on hardware based on the matching results. Developers can benefit from semantic application templates, called recipes, to fast prototype end-user applications. The evaluations confirm an engineering effort reduction by a factor of at least three compared to traditional approaches on an industrial ML classification case study, showing the efficiency and usefulness of SeLoC-ML. We share the code and welcome any contributions.Comment: Accepted by the 21st International Semantic Web Conference (ISWC2022

Enabling IoT ecosystems through platform interoperability

Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.Peer ReviewedPostprint (author's final draft

Reasoning with Large Data Sets

Abstract. Efficient reasoning is a critical factor for successful Semantic Web applications. In this context, applications may require vast volumes of data to be processed in a short time. We develop novel reasoning techniques which will extend current reasoning methods as well as existing database technologies in order to enable large scale reasoning. We propose advances and key design principles primarily in: making an efficient query execution plan as well as in memory, storage and recovery management. Our study is being implemented in Integrated Rule Inference System (IRIS)- a reasoner for Web Service Modeling Language. 1 Problem Statement The Web Service Modeling Language WSML 1 is a language framework for describing various aspects related to Semantic Web (SW) services. We are developing IRIS 2 to serve as a WSML reasoner which handles large workload efficiently. Current inference systems exploit reasoner methods developed rather for small knowledge bases [2]. These systems 3, although utilize mature and efficien