Goal-Based Semantic Queries for Dynamic Processes in the Internet of Things

Huber, Steffen; Seiger, Ronny; Kühnert, André; Theodorou, Vasileios; Schlegel, Thomas

doi:10.1142/s1793351x16400109

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Some other recent applications of QE are plagiarism detection [203], event search [89,15,43], text classification [269], patent retrieval [180,181,268], dynamic process in IoT [122,123], classification of e-commerce [128], biomedical IR [1], enterprise search [174], code search [205], parallel computing in IR [179] and twitter search [151,304]. Table 7 summarizes some of the prominent and recent applications of QE in literature based on the above discussion.…”

Section: Other Applicationsmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

228

104

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With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user's initial query is reformulated by adding additional meaningful terms with similar significance. QE -as part of information retrieval (IR) -has long attracted researchers' attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications -bringing out similarities and differences.There is a huge amount of data available on the Internet, and it is growing exponentially. This unconstrained information-growth has not been accompanied by a corresponding technical advancement in the approaches for extracting relevant information [191]. Often, a web-search does not yield relevant results. There are multiple reasons for this. First, the keywords submitted by the user can be related to multiple topics; as a result, the search results are not focused on the topic of interest. Second, the query can be too short to capture appropriately what the user is looking for. This can happen just as a matter of habit (e.g., the average size of a web search is 2.4 words [257,255]). Third, the user is often not sure about what he is looking for until he sees the results. Even if the user knows what he is searching for, he does not know how to formulate an appropriate query (navigational queries are exceptions to this [51]). QE plays an important part in fetching relevant results in the above cases. Most web queries fall under the following three fundamental categories [51, 139] :-Informational Queries: Queries that cover a broad topic (e.g., India or journals) for which there may be thousands of relevant results. -Navigational Queries: Queries that are looking for specific website or URL (e.g., ISRO).-Transactional Queries: Queries that demonstrate the user's intent to execute a specific activity (e.g., downloading papers or buying books).2 Hiteshwar Kumar Azad, Akshay DeepakCurrently, user-queries are mostly processed using indexes and ontologies, which work on exact matches and are hidden from the users. This leads to the problem of term mismatch: user queries and search index are not based on the same set of terms. This is also known as the vocabulary problem [99]; it results from a combination of synonymy and polysemy. Synonymy refers to multiple words with common meaning, e.g., "buy" and "purchase". Polysemy refers to words with multiple meanings, e.g., "mouse" (a computer device or an animal). Synonymous and polysemou...

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Section: Other Applicationsmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

228

104

View full text Add to dashboard Cite

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“…It is a free distribution method used for data mining projects, developed in 1999 by the consortium of European companies named Pete Chapman and Randy Curber (NCR -Denmark), AG (Germany), Julian Clinton, Thomas Khabaza and Colin Shearer (SPSS -England), OHRA (Holland), and Thomas Reinartz and Rüdiger Wirth (DaimlerChrysler). This method consists of six phases [12,13]:…”

Section: Crisp-dm Methodsmentioning

confidence: 99%

“…Regarding the development of the system, it was decided to opt for the OPEN UP Method because they are especially oriented to small projects, which is a tailor-made solution for that environment. For the development of the research, the CRISP-DM method was applied as a free distribution tool for data mining projects [12].…”

Section: Methodsmentioning

confidence: 99%

Early Warning System Based on Data Mining to Identify Crime Patterns

Silva

Cruz

Ureche

et al. 2019

Data Mining and Big Data

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The analysis of criminal information is critical for the purpose of preventing the occurrence of offenses, so the crime records committed in the past are analyzed including perpetrators. The main objective was to identify crime patterns in the city of Bogota, Colombia, supported using Early Warning System based on data mining (CRISP-DM method). The research results show the identification of 12 different criminal profiles demonstrating that the Early Warning System is applicable since it managed to significantly reduce the time devoted to the processes of registering complaints and searching for criminal profiles.

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“…We rely on the OpenHAB 6 middleware to connect and unify the heterogeneous set of sensors and actuators of the IoT environment. A semantic model in the knowledge base describes the properties, functionalities and relations of the individual IoT devices [96] and the associated IoT services [45]. The IoT middleware provides service-based RESTful interfaces to all devices to retrieve data and directly send commands from the HoloFlows app.…”

Section: Execution Of Iot Processesmentioning

confidence: 99%

HoloFlows: modelling of processes for the Internet of Things in mixed reality

et al. 2021

Self Cite

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Our everyday lives are increasingly pervaded by digital assistants and smart devices forming the Internet of Things (IoT). While user interfaces to directly monitor and control individual IoT devices are becoming more sophisticated and end-user friendly, applications to connect standalone IoT devices and create more complex IoT processes for automating and assisting users with repetitive tasks still require a high level of technical expertise and programming knowledge. Related approaches for process modelling in IoT mostly suggest extensions to complex modelling languages, require high levels of abstraction and technical knowledge, and rely on unintuitive tools. We present a novel approach for end-user oriented-no-code-IoT process modelling using Mixed Reality (MR) technology: HoloFlows. Users are able to explore the IoT environment and model processes among sensors and actuators as first-class citizens by simply “drawing” virtual wires among physical IoT devices. MR technology hereby facilitates the understanding of the physical contexts and relations among the IoT devices and provides a new and more intuitive way of modelling IoT processes. The results of a user study comparing HoloFlows with classical modelling approaches show an increased user experience and decrease in required modelling knowledge and technical expertise to create IoT processes.

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Goal-Based Semantic Queries for Dynamic Processes in the Internet of Things

Cited by 11 publications

References 12 publications

Query expansion techniques for information retrieval: A survey

Query expansion techniques for information retrieval: A survey

Early Warning System Based on Data Mining to Identify Crime Patterns

HoloFlows: modelling of processes for the Internet of Things in mixed reality

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