The Evolution of Contextual Information Processing in Informatics
<p>Word cloud produced by the 187 unique keywords of the 50 herein discussed papers (Created with <a href="https://wordart.com/create/ public web service" target="_blank">https://wordart.com/create/ public web service</a>).</p> "> Figure 2
<p>Diagrammatic scheme of identified contextual types.</p> ">
Abstract
:1. Introduction
2. Motivation
3. Visual Context
3.1. Low Level Context
3.2. Topological Context
3.3. Scene Context
4. Context-Awareness
5. Verbal Context
6. Short Discussion and Conclusions
Acknowledgments
Conflicts of Interest
References
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Work | Task(s) | Method | Pros | Cons | Dataset |
---|---|---|---|---|---|
[6] | semantic indexing and retrieval of videos | network of semantic objects | novel factor graph semantic modeling | not considering scene context | 8 movies, 1800 training frames, 9400 testing frames |
[7] | automatic annotation and natural object detection | supervised color classification | simplicity of approach, easy to scale | not considering scene context, small dataset | 31 annotated images |
[8] | content-based image retrieval | spatial arrangement through composite color region templates | retrieval effectiveness | not considering scene context, limited dataset | 893 (357 + 536) photographs, 10 semantic classes, 3 image queries |
[9] | sub-block classification in outdoor scenes | combination of color and texture features | context orientation information utilization | not considering scene context | -- |
Work | Task(s) | Method | Pros | Cons | Dataset |
---|---|---|---|---|---|
[10] | study of context representation in the cartographic generalization process | 3 types of context relations (group, area, surrounding objects) | proposed novel context relations | limited application field, focus only on cartography | -- |
[11] | context-aware computing survey | list-based approach, focus on wireless characteristics | in-depth contextual types analysis and modeling | limited impact , focus on mobile computing, heavily outdated | -- |
[12] | photo annotation utilization for location inference | location probability maps for tag annotations | algorithmic interpretation was packaged into an integrated system | no exploitation of tags’ context | 1 M geotagged photos |
[13] | infer location information for digital photos | use spatial, temporal & social context to generate photo metadata | combination of 3 context types, integrated system | minimal subjective evaluation (55 persons) | -- |
[14] | visual image retrieval and localization | visual and textual context similarity | fast, reliable approach, pioneer work | only basic textual context exploitation, outdated | 2 M geotagged images |
[15] | mobile app utilizing geotagged photos | emphasis on contextual familiarity | novel approach, familiar/unfamiliar places distinction | primitive context exploitation, no real evaluation, focused on mobiles | -- |
[16] | capitalization on geo-location metadata of digital images | web-based geo-coded image sharing | spatial context exploitation, context map visualization | post-capture, only spatial context type utilization | World Wide Media eXchange (WWMX) |
[17] | topological context modeling | a web-based context model | novel unstructured context model | informal context model, outdated | -- |
[18] | spatial context modeling | scene content understanding | increased accuracy of initial classification, reduced misclassifications | narrow application level | -- |
[19] | spatial context modeling | configuration based scene modeling | utilization of qualitative & photometric object relations in a spatial sense | quite outdated | -- |
[20] | spatial context-aware object-detection | combination of object detectors with spatial context | improved accuracy of natural object detection | narrow field of application, poor performance of natural object detectors | 780 images containing at least 2 object types |
Work | Task(s) | Method | Pros | Cons | Dataset |
---|---|---|---|---|---|
[21] | study on multimedia search strategies | combination of local (object) & global (scene) context | novel approach (examination of search strategies) | narrow field of application | -- |
[22] | image retrieval from large databases using image contents | utilization of color, texture, shape and layout characteristics | pioneer work, early contextual cues exploit for efficient information retrieval | nominal contextual information utilization | -- |
[23] | color and texture region extraction from images | extraction, representation and query of spatially localized regions | pioneer novel binary region representation that allowed easy indexing | utilization only of spatial contextual information | -- |
[24] | visual object detection in images | novel image representation, visual features selection, classifier combination | quick approach, high detection rate | marginal context exploitation in the sense of feature selection | real-world dataset of 130 images |
[25] | visual object detection for static images | novel (Haar wavelets) object class representation | good results shown for face, people, and car detection | very basic spatial context exploitation | 5,335,982 training patterns |
[26] | visual face detection | influence of luminance contrast, image orientation & local context | local context exploitation for face detection | poor subjective evaluation | 10 human subjects |
[27] | visual object detection, global scene recognition | utilization of scene context in the detection process | combination of global and local image features, novel utilization of scene context | very basic evaluation, no utilization of well-known datasets | 13 filters, 30 spatial templates |
[28] | identification of the most visually distinctive parts in images (salient object detection) | utilization of convolutional neural networks | contextual features exploitation in 4 different scales, performance of proposed method | focus solely on spatial coherence, no implication of other contextual types | 5 public datasets: MSRA-B, PASCAL-S, DUTOMRON, HKU-IS, SOD |
[29] | semantic scene labeling of images | introduction of long & short-term memorized contextual fusion model | fused contextual representation from multiple sources (depth & photometric data) | performance issues with respect to boundary labeling | 37 categories, large-scale public datasets: SUNRGBD (10,355 images, including NYUDv2 & SUN3D |
[30] | semantic scene classification | exploitation of 3 contextual types: spatial, temporal, and image capture condition | developed graphical context models | limitations on utilization of each context type | several real-life datasets |
Work | Task(s) | Method | Pros | Cons | Dataset |
---|---|---|---|---|---|
[31] | study systems that examine and react to an individual’s changing context | define context-aware computing, describe 4 categories of context-aware applications | definition of 4 context-aware applications, introduction of a prototype | no real evaluation of proposed model | -- |
[32] | context-awareness modeling with respect to data tailoring | survey on context modeling approaches | context model features and systems comparison | outdated work, context model bounded to a single target application | -- |
[33] | reduce information overload of large-scale information systems | context-guided data tailoring methodology | introduction of the Context Dimension Tree (CDT) model | no real evaluation of proposed model | example of 1400 contexts |
[34] | geographically contextualized personal information exploitation | link people-to-people-to-geographical-places (P3 system) | geotemporal social matching based on contextual information | sharing information & privacy concerns | 14 place types, 500+ respondents |
[35] | contextual trip planning using public transport network | context management and weighting of graph dynamic edges | applied evaluation on real-life dataset | -- | 6.962 public transport stops, 965 routes, 19.773 multigraph edges |
[36] | context-aware tools for fieldwork data processing in archaeology and other environmental sciences | generalized context view to cover physical and logical attributes of the user’s environment for field applications | perception of context as metadata, development of a pilot application | extremely limited application domain, did not conduct field trials | -- |
[37] | context-aware location identification for indoor environments | sensor-based ultrasonic system for location-aware computing | efficient object location and orientation determination | no real evaluation of proposed methodology | -- |
[38] | context-aware computing study | context-aware computing definition, identification of 3 related behaviors | early attempt to define context-aware computing | superficial discussion with no real impact on research community | -- |
[39] | intelligent Human-Computer Interaction (HCI) | proposition of a 4-stage context adaptive and intelligent HCI framework | rich behavioral interactions, nonverbal information, utilization of cognition model, computer interface adaptation | methodology has not been implemented in a running system, lack of evaluation | -- |
[40] | development of appropriate context modeling concepts for pervasive computing | proposition of a particular context model suitable for pervasive computing | generic enough to capture arbitrary types of contextual information | no evaluation or proof of concept for the proposed model | -- |
[41] | semantic context representation, context reasoning & knowledge sharing | a formal context model based on OWL ontologies | integrated system, formal and extensible context model | implementation of prototype under construction | -- |
[42] | pervasive social computing | provide taxonomy to classify pervasive social context along 4 dimensions (space, time, people, information source) | answers 5 WH (who, what, where, when, why) questions with regard to pervasive social context. | -- | -- |
[43] | data mining on “big data” from social media | “big data” mining cycle, user behavior monitoring, Twitter case study | contributions to building and running big data analytics infrastructure | minor contextual information impact within the discussed approach | -- |
[44] | organizational knowledge management, knowledge processing models | a 5-layered knowledge processing framework integrating Semantic Web with Web 2.0 | comparative analysis of relationships between Semantic Web and Web2.0 | no evaluation | -- |
[45] | business intelligence and analytics (BI&A) | a report on different types of BI&A-related context-awareness | mapping of important contextual facets of BI&A knowledge | Mostly business- than research-oriented | -- |
[46] | context-aware e-mail spam within modern social networks | context-aware e-mail analysis on Facebook to identify potential spamming | novel context-aware classification of spam into 3 types | limited, Facebook-only application domain | 7000+ randomly accessed Facebook profiles |
[47] | big data analytics with respect to the 4 Vs (volume, velocity, variety, veracity) | discussion on big data proliferation drivers & the main platforms that satisfy their 4V characteristics | detailed classification of big data challenges, recent work | -- | -- |
Work | Task(s) | Method | Pros | Cons | Dataset |
---|---|---|---|---|---|
[48] | verbal context model overview | verbal context graph | folksonomy, mathematical notation, comparison against baselines | -- | Movielens (10.681 movies) |
[49] | user interest modeling | study of 5 context sources | novel association between context types and time | study conducted solely on logs | -- |
[50] | users’ search interests prediction | activity-based context study | combination of 3 context types (queries, clicks, web-page visits) | proposed model’s simplicity | -- |
[51] | query suggestion | context-aware query suggestion model | mining latent concept patterns | -- | 3.957 M queries, 5.918 M clicks, 1.872 M query sessions |
[52] | refinement of user search queries | contextual query clustering to improve query suggestions | novel combination of contextual info (document-clicks & user sessions) | treatment of ambiguous queries | 6 months of Google.com search query logs |
[53] | study user query sequences | user behavior capture framework (vocabulary, features, baselines) | detailed evaluation, utilization of local & global features | narrow application domain (search sequences) | 1.2 M queries & 17.355 queries |
[54] | study of context in a recommender system | semantic interpretation, ontological terms, semantic relations | evaluation, novel notion of semantic runtime context | semantic ambiguities problems | 17 ontologies, 137.254 Wikipedia entries |
[55] | study of microtexts’ linguistic variation | investigation of lexical transformations properties from Twitter posts & news articles | novel methodology for contextualized analysis of lexical transformations | poor empirical evaluation, solely statistical processing | 1 M Twitter posts |
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Mylonas, P. The Evolution of Contextual Information Processing in Informatics. Information 2018, 9, 47. https://doi.org/10.3390/info9030047
Mylonas P. The Evolution of Contextual Information Processing in Informatics. Information. 2018; 9(3):47. https://doi.org/10.3390/info9030047
Chicago/Turabian StyleMylonas, Phivos. 2018. "The Evolution of Contextual Information Processing in Informatics" Information 9, no. 3: 47. https://doi.org/10.3390/info9030047
APA StyleMylonas, P. (2018). The Evolution of Contextual Information Processing in Informatics. Information, 9(3), 47. https://doi.org/10.3390/info9030047